Infrared imaging systems and methods for oil leak detection

ABSTRACT

A system for detecting an oil leak can include: at least one infrared imaging sensor; and an imaging analysis computer operably coupled with the at least one infrared imaging sensor. The imaging analysis computer can be configured to control any infrared imaging sensor and acquire infrared images therefrom at any rate and in any duration. The imaging analysis computer can be configured to analyze the infrared images in order to detect an oil leak. The imaging analysis computer can be configured to detect oil on a surface (e.g., solid surface or water surface) where oil should not be (or is not present in a baseline) in order to determine that there is an oil leak in the vicinity.

CROSS-REFERENCE

This patent application claims priority to U.S. Provisional ApplicationNo. 62/666,610 filed May 3, 2018, which provisional is incorporatedherein by specific reference in its entirety.

BACKGROUND Field

The present invention relates to systems and methods for detecting oilon surfaces. In some aspects, the present invention relates to infraredimaging systems and methods for detecting oil on solid surfaces or watersurfaces.

Description of Related Art

Generally, it is problematic to have a petroleum fluid, such as oil, onany environmental surface or industrial surfaces where it does notbelong. Leaks can occur in any component that stores or transports oil,which mandates oil leak detection. Environmental damage can be reducedor prevented with faster oil leak detection. Loss of oil from leaks alsoleaves a financial toll on the refinery. As a result, improvements inoil leak detection can be good for the environment and for reducingrefinery or other facility processing petroleum products operatingcosts.

Therefore, it would be advantageous to be able to detect oil on asurface from an oil leak. Furthermore, it would be beneficial to be ableto detect oil on solid surfaces and water surfaces.

SUMMARY

In some embodiments, a system for detecting an oil leak can include: atleast one infrared imaging sensor; and an imaging analysis computeroperably coupled with the at least one infrared imaging sensor. Theimaging analysis computer can be configured to control any infraredimaging sensor and acquire infrared images therefrom at any rate and inany duration. The imaging analysis computer can be configured to analyzethe infrared images in order to detect an oil leak. The imaging analysiscomputer can be configured to detect oil on a surface where oil shouldnot be (or is not present in a baseline) in order to determine thatthere is an oil leak in the vicinity.

In some embodiments, the system can be configured to obtain at least onebaseline infrared image of a fixed field of view without oil beingpresent. The baseline image can be updated over time prior to oil beingdetected on a surface in the fixed field of view. The baseline image canbe an image from an imaging sensor, or a historical composite of pixeldata from a plurality of baseline images over time. This allows forcomparisons between images with no oil and images that have oil (e.g.,suspected of having oil and being confirmed to have oil). Otherwise,when the current image has no oil, it is a no oil image. The protocolcontinues until an image with oil in it (e.g., oil on a surface) isobtained.

In some embodiments, the system can perform methods to analyze allpixels in the fixed field of view for changes from the at least onebaseline infrared image to at least one subsequent infrared image. Thechanges can be in the pixel data for each pixel, such as changes in thepixel data that indicates changes in temperature of surfaces emittingthe infrared light. That is, each pixel can be analyzed by analyzing thepixel data in a subsequent image and comparing that subsequent pixeldata to the baseline pixel data. The analysis can includecomputationally processing the subsequent pixel data to determine apixel value, such as a temperature for that pixel. The subsequent pixelvalue is compared to the baseline pixel value. The baseline pixel valuecan be a range of suitable pixel values, and may include a distributionof pixel values when the surface does not have oil. When the subsequentpixel value is within an allowable range of the baseline pixel value,the subsequent pixel value does not identify oil being present. However,when the subsequent pixel value is outside the allowable range of thebaseline pixel value, then a determination is made as to whether or notthe subsequent pixel value is indicative of oil being present.

In some embodiments, the system can perform methods to identify variabledifferences in temperatures for each pixel in the field of view betweenthe at least one baseline infrared image and the at least one subsequentinfrared image. The variable difference can be determined by assessingchanges in pixel temperature value for a specific pixel (e.g., pixellocation in the pixel array of the imaging device) from a baseline imageto a subsequent image. However, when the subsequent pixel temperaturevalue is outside the allowable range of the baseline pixel temperaturevalue, then a determination is made as to whether or not the subsequentpixel value is indicative of oil being present.

In some embodiments, the system can identify one or more first pixels inthe at least one subsequent infrared image having a first variabledifference in temperature that is greater than an allowable variabledifference in temperature for the one or more first pixels in the atleast one subsequent infrared image compared to an allowable variabledifference in temperature for the one or more first pixels in the atleast one baseline infrared image. Accordingly, an allowable variabledifference in temperature for each pixel can be determined, such as byrecording the pixel data for each pixel (e.g., raw pixel data ortemperature pixel data) and determining a distribution of pixeltemperatures for each pixel. The distribution of pixel temperatures,based on historical pixel temperatures, can evolve as more pixel data isobtained for each pixel without oil. The distribution of pixeltemperatures can used to set a threshold temperature for a pixeltemperature, where the threshold temperature sets an upper boundary forthe allowable variable difference in temperature. The pixel temperaturefor each pixel in the subsequent image can be compared to the thresholdtemperature so as to be compared to the allowable variable difference intemperature. Then, pixels in the subsequent image having a pixeltemperature greater than the threshold temperature are identified asbeing outside the allowable variable difference in temperature.

In some embodiments, the system can determine that there are one or morefirst pixels as being oil based on the first variable difference intemperature of the one or more first pixels being greater than theallowable variable difference in temperature of the one or more firstpixels in the fixed field of view. As such, pixels having a pixeltemperature that is greater than the threshold temperature can beidentified as being oil due to having the first variable difference intemperature that is greater than the allowable variable difference intemperature for each pixel. The pixels having a pixel temperature thatis outside or larger than the allowable variable difference intemperature can be identified as being oil.

In some embodiments, the system can generate an alert that identifiesoil being present in the fixed field of view. This is done when one ormore pixels are identified as having oil.

In some embodiments, the system can perform methods to identify one ormore first pixels in the at least one subsequent infrared image having afirst variable difference in temperature that is greater than a secondvariable difference in temperature for one or more second pixels in theat least one subsequent infrared image compared to the at least onebaseline infrared image. The region of the first pixels can be analyzedto determine the temperature in the baseline image and the subsequentimage, and then determine the change in temperature. Then, the region ofthe second pixels can be analyzed to determine the temperature in thebaseline image and the subsequent image, and then determine the changein temperature. The change in temperature for the first pixels iscompared to the change in temperature for the second pixels. When onegroup of pixels changes more than the other, then it can be determinedthat the surfaces of those pixels changed.

In some embodiments, the system can perform methods to determine the oneor more first pixels as being oil and the one or more second pixels asbeing devoid of oil. This determination can be made based on the firstvariable difference in temperature of the one or more first pixels andthe second variable difference in temperature of the one or more secondpixels in the fixed field of view. When the change in the first pixelsis larger than the change in the second pixels, there is an indicationthat oil is on the surface in the first pixels. Regions where thetemperature variance is similar from the baseline infrared images to thesubsequent images indicate that there hasn't been a change to thesurfaces, and they do not have oil on them.

In some embodiments, the system can perform methods to generate an alertthat identifies the presence of oil in the fixed field of view. In someaspects, the imaging analysis computer is configured to provide thealert. In some aspects, the imaging analysis computer is configured toprovide the alert by actuating an audible and/or visible indicator. Insome aspects, the imaging analysis computer is configured to provide thealert by transmitting the alert to a remote device. In some aspects, thealert is an audible or visible communication.

In some embodiments, the system can perform methods to identify a one ormore first pixels having a variable difference in temperature of from0.5° C. to about 2° C. higher than one or more second pixels in the atleast one subsequent infrared image. A variable difference in this rangefor a group of pixels can indicate the presence of oil. In someinstances, the range may be from 0.25° C. to about 3° C. higher, 0.1° C.to about 2.5° C. higher, or other range indicative of oil being present.

In some embodiments, the system can perform a method for detectingviscosity of oil. The method can include: obtaining at least onebaseline infrared image of a fixed field of view without oil beingpresent; analyzing all pixels in the fixed field of view for changesfrom the at least one baseline infrared image to at least one subsequentinfrared image; identifying variable differences in temperatures foreach pixel in the field of view between the at least one baselineinfrared image and the at least one subsequent infrared image;identifying one or more first pixels in the at least one subsequentinfrared image having a first variable difference in temperature that isgreater than an allowable variable difference in temperature for the oneor more first pixels in the at least one subsequent infrared imagecompared to an allowable variable difference in temperature for the oneor more first pixels in the at least one baseline infrared image;determining the one or more first pixels as being oil based on the firstvariable difference in temperature of the one or more first pixels beinggreater than the allowable variable difference in temperature of the oneor more first pixels in the fixed field of view; determining anestimated viscosity of the oil in the one or more first pixels based ona comparison of the determined variable difference with viscosity datathat correlates a variable difference in temperature with a viscosity,wherein the viscosity data includes a defined lower viscosity thresholdvalue and a defined upper viscosity threshold value, wherein theestimated viscosity is interpolated between the lower viscositythreshold value and the upper viscosity threshold value; and generatinga report that identifies the estimated viscosity for the oil in thefixed field of view.

The foregoing summary is illustrative only and is not intended to be inany way limiting. In addition to the illustrative aspects, embodiments,and features described above, further aspects, embodiments, and featureswill become apparent by reference to the drawings and the followingdetailed description.

BRIEF DESCRIPTION OF THE FIGURES

The foregoing and following information as well as other features ofthis disclosure will become more fully apparent from the followingdescription and appended claims, taken in conjunction with theaccompanying drawings. Understanding that these drawings depict onlyseveral embodiments in accordance with the disclosure and are,therefore, not to be considered limiting of its scope, the disclosurewill be described with additional specificity and detail through use ofthe accompanying drawings.

FIG. 1 includes a schematic diagram for a system for monitoring anenvironment with a set of infrared imaging sensors arranged formonitoring surfaces of components of an oil processing system and thesurrounding area.

FIG. 2 shows a graphical user interface for monitoring the imagesobtained from the imaging sensors in order to determine whether or notoil is present in the field of view.

FIG. 3 is a flow chart of a process of one exemplary embodiment of themethods for detecting oil that can be performed by the embodiments ofthe systems disclosed herein.

FIG. 4 is a flowchart of a process of one exemplary embodiment of amethod for determining temperature values for pixels in an infraredimage that can be performed by the embodiments of the systems disclosedherein.

FIG. 4A is a flowchart of a process for generating a historicalvariation map.

FIG. 4B includes a flowchart of a process of generating a category mapfor the variation in temperatures for each pixel.

FIG. 4C includes a flowchart of a process of generating an alert basedon an abnormal region of pixels that are identified as being a region ofoil.

FIG. 5A illustrates a method of detecting an oil leak.

FIG. 5B includes a method for detecting oil on a surface.

FIG. 5C includes a method for detecting oil on a water surface.

FIG. 5D shows a method for detecting oil on a surface.

FIG. 5E shows another method for detecting oil on a surface.

FIG. 5F show another method for determining that a surface has oil.

FIG. 6 shows an example computing device (e.g., a computer) that may bearranged in some embodiments to perform the methods (or portionsthereof) described herein.

FIG. 7 illustrates a method of determining viscosity of oil.

FIG. 8 provides an example infrared image that shows a control regionwithout oil on the surface having a first temperature and an oil regionwith oil on the surface having a second temperature.

The features of the figures can be arranged in accordance with at leastone of the embodiments described herein, and which arrangement may bemodified in accordance with the disclosure provided herein by one ofordinary skill in the art.

DETAILED DESCRIPTION

In the following detailed description, reference is made to theaccompanying drawings, which form a part hereof. In the drawings,similar symbols typically identify similar components, unless contextdictates otherwise. The illustrative embodiments described in thedetailed description, drawings, and claims are not meant to be limiting.Other embodiments may be utilized, and other changes may be made,without departing from the spirit or scope of the subject matterpresented herein. It will be readily understood that the aspects of thepresent disclosure, as generally described herein, and illustrated inthe figures, can be arranged, substituted, combined, separated, anddesigned in a wide variety of different configurations, all of which areexplicitly contemplated herein.

Generally, the present technology provides a system and method fordetecting an oil leak that can include at least one infrared imagingsensor and an imaging analysis computer operably coupled with the atleast one infrared imaging sensor. The imaging analysis computer can beconfigured to control any infrared imaging sensor and acquire infraredimages therefrom at any reasonable rate and in any duration. The imaginganalysis computer can be configured to analyze the infrared images inorder to detect an oil leak. The imaging analysis computer can beconfigured to detect oil on a surface where oil should not be (or is notpresent in a baseline) in order to determine that there is an oil leakin the vicinity.

In some embodiments, the system can be an infrared monitoring system.The system can include a thermal imaging device (for example, aninfrared (IR) imaging device) and a processor that are collectivelyconfigured to monitor and detect oil leaks. In some embodiments, thesystem may monitor a fixed field of view to detect oil on hard surfacesand separately to detect oil on water. If oil is detected, the system isconfigured to alert a user to the presence of the oil leak (or apotential oil leak). For example, by actuating an indicator (e.g., avisual alarm or an audio alarm) and/or by communicating to one or moreusers via an electronic communication channel (e.g., text message,email, telephone call, etc.). In some embodiments, an IR monitoringsystem (or at least an IR detector sensor or device) may be positionedunder pumps, around flanges or connector pipes, etc. in a refinery. Insome embodiments, an IR monitoring system may be used to detect oil onwater, for example, in jetty areas or on an offshore oil terminal,around the terminal and fuel carrying ships to detect oil on water.

In some embodiments, a process (or a system) may start with a baselineIR image of the monitored field-of-view (FOV) without oil being present.The process may analyze all pixels in the FOV for changes from thebaseline image to a subsequent image in order to detect oil based onvariable differences in thermal temperatures of each pixel. Oil can beabout 0.5 to about 2° C. warmer than surfaces that are not coated withoil; however, it should be recognized that this temperature differencevariation may be different in different ambient conditions, differentgeographical locations, different humidity, or different times of theday, month, season or year. Also, each pixel is well characterized inthe absence of oil, such as each pixel being related to surface data fora surface in the pixel. The well characterized pixel can have a range ofsuitable pixel values when there is not oil, so that the presence of oilshows a significantly different pixel value. The significantly differentpixel value can be used to determine that there is now oil on thesurface that is causing the different pixel value.

The process may also determine the type of oil and viscosity based onthe difference in temperature variance as the thicker the oil the largerthe variance in temperature from base surface (e.g., control region) tothe oil (e.g., test region). A separate process may be used for oil onwater compared to oil on solid surface. Some embodiments may include anoption for rain detection which will trigger the use of the oil on waterprocess as a dry surface changes to a wet surface.

FIG. 1 includes a schematic diagram for a system 100 for monitoring anenvironment 102 with a set of infrared imaging sensors 104 arranged formonitoring surfaces 106 of components 108 of an oil processing system110 and the surrounding area 112. The system 100 also includes an imageanalysis computer 114 operably coupled to the set of infrared imagingsensors 104 through a network 116 (e.g., wired, wireless, optical or anynetwork) represented by the dashed box. This allows for the infraredimaging sensors 104 to send infrared image data over the network 116 tothe image analysis computer 114 for analysis.

While FIG. 1 shows four imaging sensors 104 positioned in theenvironment 102 around the oil processing system 100, the number ofimaging sensors 104 included in the disclosed systems and/or operated inthe disclosed methods may vary per embodiment. In some aspects, it maybe desirable to achieve 360° coverage of the components 108 in the oilprocessing system 110 so as to detect oil 120 on any surface of acomponent 108 or in various locations to monitor the components 108 aswell as the environment 102 (e.g., industrial environment, naturalenvironment, etc.). In some aspects, systems 100 can include 4, 5, 6, 7,8, 9, or 10 or more infrared imaging sensors 104 positioned around anoil processing system 110. As some components 108 of an oil processingsystem 110 may be of substantial height or length, in some aspects, itmay be desirable to position a first set of imaging sensors 104 toprovide coverage of a first area, and a second set of imaging sensors104 to provide coverage for a second area. Depending on the length orheight of the components being monitored, the number of imaging sensors104 employed in various embodiments can vary substantially.

The imaging sensors 104 can be any infrared sensor. For example, theimaging sensor can be a long wave IR thermal machine vision camera(e.g., FLIR A615), which can include streaming an image frequency of 50Hz (100/200 Hz) with windowing, an uncooled microbolometer, 640×480pixels, 17 micron detector pitch, 8 ms detector time constant, andoperational temperature over −20 to 150° C. The infrared imaging sensorcan produce radiometric images with radiometric data for each pixel. Insome aspects, the infrared imaging sensor can detect temperaturedifferences as small as 50 mK, which provides accuracy even at longerdistances. The infrared imaging sensor can provide 16 bit temperaturelinear output. The imaging sensor can provide the radiometric data as orabout 307,200 pixels in infrared images with embedded temperaturereadings with the radiometric images. The imaging sensors 104 mayinclude a weatherproof housing (e.g., wind and/or rain tight), which maybe configured as spark proof or explosion proof housing. As such, thehousing of the shown image sensors may be configured to be explosionproof as known in the art (e.g., solid anti-corroding aluminumconstruction, epoxy polyester powder paint, germanium window, dustproof, water proof, explosion proof, and optionally with a heater).

In some aspects, the radiometric data/images from the infrared sensor(e.g., radiometric IR camera) produces at least 16 bits of infrared dataper pixel. These radiometric data/images can be used by the imaginganalysis computer reading or recording the ‘count’ data (e.g., 16 bits)for each pixel, which when converted represents the thermal temperatureof the pixel. This feature of using radiometric data/images providesmore information for the present invention compared to IR images thatare just JPEG images (e.g., non-radiometric data) from IR cameras thatdon't contain any thermal data and instead rely on image comparisons todetect change.

In some embodiments, discussion of images or infrared images isconsidered to be radiometric digital data from a long wave IR camera sothat the algorithms process the radiometric digital data. The use ofradiometry can use temperature measurement data for each pixel, wherethe radiometric measurements can be used for reading the intensity ofthermal radiation, which can be used for temperature determination foreach pixel. The radiometric thermal data for each pixel with pixelvalues correspond to the temperature of the scene. The radiometric dataprovides a precise temperature, which allows for external sceneparameters to be compensated for emissivity (e.g., a measure of theefficiency of a surface to emit thermal energy relative to a perfectblack body source) and window transmission to more accurately determinetemperature. The user (or imaging analysis computer) may obtaintemperature data from the radiometric data, as well as maximumtemperatures, minimum temperatures, and standard deviations foruser-defined regions (points of interest) for one or more pixels or aplurality of pixels.

Some radiometric IR cameras have the ability to compensate forvariations in camera temperature. This allows operators of the systemsto receive output from the radiometric IR cameras that has beenstabilized and normalized, resulting in temperature-stable images orvideo. As a result, a scene with a given temperature can correspond to acertain digital value in the image or video, independent of the camera'stemperature. In some aspects, it can be important to distinguishtemperature measurements as surface infrared measurements becauseradiometric measurements can measure surface temperatures. Metals, andorganic material (like people), are usually completely opaque, andradiometric measurements can be able to resolve their surfacetemperature. Remote temperature sensing of a surface relies on theability to accurately compensate for surface characteristics,atmospheric interference, and the imaging system itself. The surfacecharacteristics that influence temperature measurement are surfaceemissivity and reflectivity at the infrared spectral wavelengths, whichcan be considered in the algorithms and data processing describedherein.

In some aspects, the imaging sensors 104 may be infrared imaging sensorsthat provide radiometric data/images. Infrared imaging sensors maycapture wavelengths of light between at least 700 nanometers to 1millimeter, and indicate the captured wavelengths in digital imageinformation transmitted over the network 116 to the image analysiscomputer 114. Upon receiving the digital image information from theimaging sensors 104, the image analysis computer 114 may analyze theimage information to determine temperature information for each pixel inthe digital image. An operator of the system 100 may establish one ormore warning levels or alert levels for one or more regions of interest(e.g., one or more pixels or combinations of adjacent pixels) within thedigital image information of the digital images. The image analysiscomputer 114 may generate one or more warnings and/or alerts if theestablished alerting levels are exceeded. This may enable an operator toidentify problems with the operation of the oil processing system 110,such as an oil leak, earlier than previously possible, resulting in lessdamage to the environment 102 or the oil processing system 110 andreduced production outages. Identifying and fixing oil leaks can beeconomically beneficial to the entity operating the oil processingsystem 110.

FIG. 1 also shows the imaging analysis computer 114 with a display 118that can provide a user interface for monitoring images from the imagingsensors 104 and data obtained from computations of the digital imageinformation in the images obtained during the monitoring protocols.

FIG. 2 shows a graphical user interface 200 for monitoring images 205obtained from the imaging sensors 104 in order to determine whether ornot oil is present in the field of view. The data processing protocolscan be performed by the imaging analysis computer 114 so that visualinformation in the graphical user interface 200 can be provided on thedisplay 118 for an operator of the system 100.

The images 205 can be parsed into environmental areas 202 and industrialareas 204. The image 205 can be parsed to show positive control areas207 with oil leaks and/or negative control areas 209 without oil leaksor oil on a surface. Any of these may be labeled as a region ofinterest.

The images 205 can be parsed into one or more regions of interest 210and identified by boundary indicators, such as a frame or window aroundeach region of interest 210. The regions of interest 210 can bedetermined by the operator and input into the imaging analysis computer114, or by the imaging analysis computer 114 analyzing prior selectedregions of interest 210 and determining pixels commonly present in theregions of interest 210 to be a region of interest (e.g., based onhistorical data from images 205).

In some aspects, the image 205 may be received from a single imagingsensor 104, such as at any one of the imaging sensor 104 locations shownin FIG. 1. In some aspects, the image 205 may be generated by stitchingtogether two or more images from two or more imaging sensors 104, suchas any two or more of infrared imaging sensors or infrared imagingsensors combined with visible spectrum cameras. The image or videostitching of images from multiple imaging sensors may be performed byany of the methods known in the art. For example, in some aspects,OpenCV may be used to perform video stitching. Some aspects may utilizeVideo Stitch Studio by Video Stitch of Paris, France. Other aspects mayuse other methods.

The graphical user interface 200 can include input controls, cameracontrols, display controls, image controls, region of interest (ROI)controls, threshold controls, and alarm controls in order to allow theoperator to control substantially any aspect of the monitoring protocol.The operator can: select which camera or combinations of cameras arebeing displayed by the input controls, select the field of view with thecamera controls, select how the image from the camera looks on thedisplay with the display controls, select the scaling or other imageadjustments with the image controls, select various ROIs with the ROIcontrols, select temperature thresholds for one or more pixels or groupsof pixels in the images with the threshold controls, and select one ormore alarm levels and alarm display types (e.g., audible and/or visible)with the alarm controls. Over time, the data input into the graphicaluser interface 200 can be monitored and registered with the imaginganalysis computer 114, and the input data can be analyzed to determinean automated operating protocol that is performed automatically by theimaging analysis computer 114 based on historical operations. Theoperator can adjust any operational parameter on the fly to update theautomated operating protocol.

In some embodiments, the graphical user interface 200 also includes ascale indicator, a warning threshold control, and an alert thresholdcontrol. The scale indicator determines a graphical resolution ofsurface temperature ranges rendered within a region of interest of theimage 205. For example, a smaller or narrower temperature range mayprovide an image that can communicate more fine detail between surfacetemperatures of the image (e.g., between a surface with or without oil).

The graphical user interface 200 can be operated by the warning andalert threshold controls being operated by an operator in order to setindependent thresholds for warning indicators (e.g., possible oil) andalert indicators (e.g., oil spill detected). The example shown in FIG. 2may provide a warning threshold at a temperature of 0.5° C. variationbetween different regions of interest, and an alert threshold at atemperature of 2° C. variation between different regions of interest.

The graphical user interface 200 can also include a temperature variancestatus indicator, which can be shown as a probability of oil (e.g., on asurface) in a region of interest. The oil presence status indicator caninclude a minimum, maximum, and average temperature variance (e.g.,shown as probability of oil) currently detected within selected regionsof interest 210, such as a known dry surface without oil and a problemarea with prior oil leaks (e.g., flange junction, joints, etc.) Thealert window shows alerts when the minimum, maximum, or averagetemperature variance (e.g., shown as probability of oil) shown in thestatus indicator have exceeded either of the warning or alertthresholds. Different flashing lights (e.g., different color), alarmsounds (e.g., different volume or sound pattern or word notificationsvia speakers), or combinations may be provided.

The graphical user interface 200 can also include a flying spotindicator. The flying spot indicator provides an indication of atemperature or probability of oil at a position (or pixel) in the image205 that a pointing device may be hovering over.

Each region of interest 210 may include its own separate parameters,such as a scale indicator, warning and alert thresholds, temperaturevariance status, probability of oil indicator, and others. By selectingeach of the regions of interest 210 individually, the display of thegraphical user interface 200 may switch so as to display parameterscorresponding to the selected region of interest. To edit one or moreparameters for a region of interest, the region of interest is selected,for example, via a pointing device such as a mouse by clicking on theregion of interest 210. The parameters corresponding to that selectedregion of interest are then displayed, and may be edited directly viathe graphical user interface 200.

As discussed above, in some aspects, the image 205 may be generated bystitching together images captured by multiple imaging sensors 104.Graphical user interface 200 can be modified providing for themanagement of images from multiple imaging cameras 104. A graphical userinterface 200 can include a camera selection field, region name fieldand link to region field. The camera selection field allows auser/operator to select between a plurality of imaging sensors, such asimaging sensors 104, that may be under control of, for example, theimage analysis computer 114. When a particular imaging sensor 104 isselected in the camera selection field, the image 205 shown in thegraphical user interface 200 may be received from the selected camera.In a particular embodiment, each region of interest shown in the image205, such as the regions of interest 210, may be imaging sensorspecific. In other words, the system 100, or more specifically the imageanalysis computer 114, may maintain separate parameters for each imagingsensor 104 utilized by the system 100. The separate parameters mayinclude the number, names (see below) and configurations of regions ofinterest for each imaging sensor, warning and alert levels for eachregion of interest, and any linking between regions of interest, bothwithin an image captured by one imaging sensor or across multiple imagescaptured by multiple imaging sensors. A list of imaging sensorsavailable for selection in the camera selection field may be generatedbased on configuration data providing the list of imaging sensors andindications of how imaging data may be obtained from the listed imagingsensors.

The region name field allows each region of interest 210, such as thosewith common oil leaks or known small leaks, to be named by an operatorto allow for easy tracking and monitoring. The value in the region namefield may change as each region of interest 210 is selected so as todisplay a name associated with the selected region of interest. Thus,region name field may be a read/write field, in that a current value isdisplayed but can be overwritten by an operator, with the overwrittenvalue becoming the new current value. Regions that may not have oil canbe named as controls so that the temperature variance is determined withknown surfaces without oil.

The image analysis computer 114 can be provided in variousconfigurations from standard personal computers to cloud computingsystems. FIG. 6, described in more detail below, provides an example ofan image analysis computer 114, and includes the features of a standardcomputer. The image analysis computer 114 may communicate with theimaging sensors 104. For example, the image analysis computer 114 may beconfigured to transmit one or more configuration parameters to one ormore of the imaging sensors 104, and command the imaging sensors 104 tocapture one or more images. The image analysis computer 114 may furtherbe configured to receive digital images from the imaging sensors 104,capturing different perspectives of a scene or environment.

The image analysis computer 114 may store instructions that configurethe processor to perform one or more of the functions disclosed herein.For example, the memory may store instructions that configure theprocessor to retrieve an image from the imaging sensor(s) 104 anddisplay the image on the electronic display 118. The memory may includefurther instructions that configure the processor to define one or moreregions of interest in one or more images captured by one or moreimaging sensors 104, and monitor temperatures, temperature variances, orpossibility of oil being present in the regions of interest throughsuccessive images captured from the imaging sensor(s) 104. In someaspects, the memory may include instructions that configure theprocessor to set warning and/or alert threshold values for temperatureswithin one or more regions of interest defined in the image(s) of thescene or environment or defined or fixed fields of view of each camera,and generate warnings and/or alerts that oil may be present or ispresent when those threshold values are exceeded.

FIG. 3 is a flow chart of a process 300 of one exemplary embodiment ofthe methods for detecting oil that can be performed by the embodimentsof the systems disclosed herein. The process can include obtaining an IRimage (step 302) from the image data from the imaging sensors 104, whichcan be stitched together to form an image 205. In some aspects, theimage 205 may be generated based on image data from only a singleimaging sensor, or more than two imaging sensors. The image 205 includesan array of pixels, each pixel having a pixel value. Each pixel valuerepresents light captured at a position corresponding to the pixel'slocation within the pixel array. The field of view may be fixed, andthereby each pixel can have a defined pixel location in the array thatcorresponds to a surface of the field of view. The image 205 is thenprocessed to determine pixel temperature values (step 304), whichdetermines temperatures for each pixel based on the pixel values in theimage 205. The process can create a temperature map for each image (step306), where each pixel in the temperature map has a corresponding pixeltemperature data. In some aspects, for each pixel value in the image205, there is a corresponding temperature value in the temperature map.A temperature map can be generated for each IR image. The process cananalyze the temperature values included in the temperature maps acrossat least two images, and preferably across a plurality of images overtime, in order to identify a historical temperature variance for eachpixel (step 308). This provides a range of historical temperatures, ahistorical temperature variance, over time to show how the temperatureof each pixel can vary over time when there is no oil for the pixel. Forexample, a first pixel may represent a first surface, and thetemperature of that surface can vary due to changing ambienttemperatures, such as throughout the day, or across weeks, months, orseasons. The surface temperature is allowed to vary without there beingan indication of oil, such as by varying within an allowable variationin temperatures. The historical variation of pixel temperatures for eachpixel are aggregated to produce a historical temperature variation map(step 310) that includes an allowable range of temperatures for eachpixel. The temperature variation map may include a value or range ofvalues for each temperature variation for each pixel in the temperaturemap. As such, the historical variation map shows the historicaltemperature variation over a time period. The temperature map, for acurrent IR image, is then compared to the historical variation map, suchas by each pixel in the temperature map being compared to thecorresponding pixel in the historical variation map (step 312). Thecomparison results in the current temperature for a pixel being lessthan, the same, or greater than a value in the historical variation mapto generate a category map (Step 314). When the current temperature fora pixel is greater than a value in the historical variation map, thepixel is categorized as abnormal (e.g., oil) in the category map.Otherwise, when the current temperature is less than or the same as thevalues in the historical variation map, the pixel is categorized asnormal (e.g., not oil) Each value in the category map may indicatewhether a corresponding temperature value in the temperature map iswithin a normal range or is categorized as abnormal with respect to thehistorical variation map, which includes data for each pixel for theallowable variation in temperature. When categorized as abnormal, theprocess can determine whether there is an oil region by linking adjacentpixels that are categorized as abnormal (step 316). After the categorymap is generated one or more abnormal regions are determined to be oilregions by processing the data. Based on the abnormal regions being oilregions, the process 300 can generate one or more alerts (step 318).While process 300 is serialized in the preceding discussion, one ofskill in the art would understand that at least portions of process 300may be performed in parallel in some operative embodiments.

FIG. 4 is a flowchart of a process 400 of one exemplary embodiment of amethod for determining temperature values for pixels in an infraredimage that can be performed by the embodiments of the systems disclosedherein. In block 402, a pixel value for an image from an infrared sensoris obtained. In some aspects, the image may be captured from one of theimaging sensors 104, discussed above with respect to FIG. 1. In someaspects, one or more of the imaging sensors 104 may record wavelengthsof light between 700 nanometers and 1 mm (infrared wavelengths) alongwith intensity, brightness, or other light parameter, and represent thecaptured light as a digital image with each pixel having pixel data(e.g., pixel value). The pixel value received in block 402 may be onepixel value from an array of pixel values included in the capturedimage, where each pixel can include the pixel value

In block 404, a depth value corresponding to the pixel value is obtainedfor the pixel (or each pixel). In some aspects, the depth value may beobtained from a depth map of the image. The depth map may be obtained,in some aspects, via a ranging device, such as a radio detection andranging (RADAR) or light and radar or LIDAR device. In some aspects, thedepth map may be obtained using structured light. The depth map may beobtained by known methods, and may be used due to the fixed field ofview, where each pixel can be mapped with the distance to the surface inthe fixed field of view that corresponds with the pixel.

In block 406, an emissivity value corresponding to the pixel value isobtained. In some aspects, the emissivity value may be based on asetting of the imaging sensor referenced in block 402. For example, insome aspects, the imaging sensor may be configured to capture objects ofa given emissivity for each pixel. That is, a surface that correspondsto a pixel can have an emissivity value. This emissivity value may beused in block 406. In some aspects, an object database may include theemissivity of known objects. In some aspects, an emissivity value of anobject being searched for in the image may be used. For example, in someaspects that may be imaging a steel pipe, an emissivity of steel may beused for the pixels that correspond with the steel pipe. This allows forthe image to include a plurality of surfaces, and each pixel cancorrespond to a specific surface with the specific emissivity of thatsurface. As such, emissivity for various objects (e.g., from surface ofthe object) can be obtained, where the objects can be natural plants inthe environment or concrete, gravel, metals, plastics or otherindustrial surfaces. The emissivity of different types of oil may alsobe obtained for the data analysis so that oil can be identified as wellas the viscosity of the oil being identified. This can allow fordetermining the type of oil. This emissivity value may be configured byan operator in some aspects.

In block 408, a temperature value corresponding to the pixel value isdetermined based on the corresponding depth value and emissivity value.In some aspects, block 408 may include translation of a raw value fromthe imaging sensor into a power value. For example, in some aspects, theimaging sensor may provide imaging values in digital numbers (DNs). Insome aspects, the power value may be determined using Equation 1:

Power=(Raw Signal Value−Camera Offset)/Camera Gain  (1)

A signal value may be determined by Equation 2 below:

Signal=K ₁×power−K ₂,

wherein:

$\mspace{79mu} {{K_{1} = \frac{1}{\tau_{Atm} \times {Emissivity} \times {ExtOptTransm}}},{K_{2} = {\frac{1 - {Emissivity}}{{Emissivity} \times {AtmObjSig}} + \frac{1 - \tau_{Atm}}{{Emissivity} \times \tau_{atm} \times {AtmObjSig}} + \frac{1 - {ExtOptTransm}}{{Emissivity} \times \tau_{Atm} \times {ExtOptTransm} \times {ExtOptTempObjSig}}}}}$

t_(ATM) is the transmission coefficient of the atmosphere between thescene and the camera, and is a function of spectral response parameters,object distance, relative humidity, etc.

ExtOptTransm is the External Optics Transmission and is the transmissionof any optics (e.g. a protective window) between the object being imagedand the optics of the imaging sensor. The external optics transmissionis a scalar value between zero and one. External optics that do notdampen the measurement have a value of one, and optics that completelysampan the measurement have a value of zero.

ExtOptTempOjbSig is the temperature of any optics (e.g., a protectivewindow) between the object being imaged and the optics of the camera.

Emissivity is the emissivity of the object whose temperature is beingdetermined.

To convert the signal calculated via Equation 2 into a temperature, someimplementations may use Equation 3:

$\begin{matrix}{{Temperature} = \frac{B}{\log ( {\frac{R}{Signal} + F} )}} & (3)\end{matrix}$

where B, R, and F may be calibration parameters retrieved from theimaging sensor. The temperature may be in Celsius or Kelvin.

Also, a model for the total radiation W_(tot), incident on the imagingsensor can be determined by the following Equation 4 by:

W _(tot)=ε_(obj)τ_(atm)τ_(extopt) W _(obj)+(1−ε_(obj))τ_(atm)τ_(extopt)W _(amb)+(1−τ_(atm))τ_(extopt) W _(atm)+(1−τ_(extopt))W _(extopt)   (4)

In this equation, the ε_(obj) is the emissivity of the object beingimaged; τ_(Atm) and τ_(extopt) are the transmittance of the atmosphereand external optics, respectively; and W_(obj), W_(amb), W_(atm), andW_(extopt) are the radiation from the object, ambient sources,atmosphere, and external optics, respectively. The emissivity ε_(obj) ofthe object is known or assumed prior to imaging the object. Thetransmittance τ_(atm) of the atmosphere is a function of the measuredrelative humidity ϕ and temperature Tat, of the atmosphere, and themeasured distance d_(obj) from the sensor to the object. Thetransmittance τ_(extopt) of the external optics is typically estimatedduring a calibration procedure that occurs prior to imaging the object.

Given the temperature T_(obj) of the object, and the measuredtemperature T_(amb) of the ambient sources, temperature T_(atm) of theatmosphere, and temperature T_(extopt) of the external optics; theradiation W_(obj) from the object, radiation W_(amb) from the ambientsources, radiation W_(atm) from the atmosphere, and radiation W_(extop)from the external optics, respectively, are calculated using Planck'slaw, which describes the radiation W emitted at wavelength λ by a blackbody at temperature T and is given by Equation 5.

$\begin{matrix}{W = {\frac{2\; \pi \; {hc}^{2}}{\lambda^{5}}\frac{1}{{\exp ( \frac{hc}{\lambda \; k_{B}T} )} - 1}}} & (5)\end{matrix}$

In Equation 5, h is the Planck constant, c is the speed of light in themedium (a constant), and k_(B) is the Boltzmann constant.

Additionally, the IR camera maps the total radiation W_(tot) to imageintensities (i.e., pixel values) I=ƒ(W_(tot)) under the radiometricresponse function ƒ of the camera, which is typically estimated during acalibration procedure that occurs prior to imaging the object.

The above model of the image formation process may be used to solve forthe temperature T_(obj) of the object, given all of the other variables,as follows. Given an image I of intensities acquired by the camera, thetotal radiation W_(tot)=ƒ⁻¹(I) (i.e., image intensity maps to incidentradiation under the inverse of the camera response function). Then,solving equation 1 for the radiation W_(obj) from the object yieldsEquation (6).

$\begin{matrix}{W_{obj} = \frac{W_{tot} - \begin{bmatrix}{{( {1 - ɛ_{obj}} )\tau_{atm}\tau_{extopt}W_{amb}} +} \\{{( {1 - \tau_{atm}} )\tau_{extopt}W_{atm}} + {( {1 - \tau_{extopt}} )W_{extopt}}}\end{bmatrix}}{ɛ_{obj}\tau_{atm}\tau_{extopt}}} & (6)\end{matrix}$

Then, Equation 6 is solved for the temperature T_(obj) of the object asEquation 7.

$\begin{matrix}{T_{obj} = {\frac{hc}{\lambda \; k_{B}}\frac{1}{\log ( {\frac{2\; \pi \; {hc}^{2}}{\lambda^{5}W_{obj}} + 1} )}}} & (7)\end{matrix}$

In block 410, the determined temperature value is stored in atemperature map, such as in step 306. The temperature map may be used asinput for one or more of the processes discussed herein. A temperaturemap may be a data structure that stores temperature values for at leasta portion of pixels in an image or region of interest. In some aspects,the temperature map may be stored in the memory of the image analysiscomputer 114.

Decision block 415 determines whether there are additional pixels toprocess in the image (or region of interest). If there are additionalpixels, processing returns to block 402. Otherwise, processing continuesin order to determine whether or not oil is present in any of theimages.

FIG. 4A includes a flow chart of a process 470 of generating ahistorical variation map for the variation in temperatures for eachpixel. The process 470 can include obtaining a plurality of historicalpixel temperatures for a first pixel (step 472). The plurality ofhistorical pixel temperatures for a first pixel are grouped in adistribution of historical pixel temperatures for the first pixel (step474). A threshold difference (D) is determined based on the distributionof historical pixel temperatures (step 474), wherein the thresholddifference D is the maximum allowed difference from the distribution ofhistorical pixel temperatures that the pixel can have based on thehistorical temperature data for that pixel. The threshold difference Dis then combined with the distribution of historical pixel temperaturesto determine the threshold temperature (TT) (step 476). The thresholdtemperature TT is then combined with the distribution of historicalpixel temperatures to determine an allowable difference in temperature,which allowable difference in temperature is set as the historicalvariance in temperature (step 478). The historical variation map canthen be prepared to include the allowable difference in temperature orthe historical variance for each pixel (step 480). The process cananalyze the temperature values included in the temperature maps acrossat least two images, and preferably across a plurality of images overtime, in order to identify a historical temperature variance for eachpixel (step 308). This provides a range of historical temperatures, ahistorical temperature variance, over time to show how the temperatureof each pixel can vary over time when there is no oil for the pixel. Forexample, a first pixel may represent a first surface, and thetemperature of that surface can vary due to changing ambienttemperatures, such as throughout the day, or across weeks, months, orseasons. The surface temperature is allowed to vary without there beingan indication of oil, such as by varying within an allowable variationin temperatures. The historical variation of pixel temperatures for eachpixel are aggregated to produce a historical temperature variation map(step 310) that includes an allowable range of temperatures for eachpixel. The temperature variation map may include a value or range ofvalues for each temperature variation for each pixel in the temperaturemap. As such, the historical variation map shows the historicaltemperature variation over a time period.

FIG. 4B includes a flow chart of a process 420 of generating a categorymap for the current temperatures for each pixel based on the historicalvariation of each pixel. The historical variation map may indicateacceptable ranges of pixels that are within a normal range (e.g., notoil) and unacceptable ranges of pixels that are outside the normal range(e.g., oil). The pixels outside the normal range can be analyzed todetermine whether or not they include oil.

In the illustrated embodiment, process 420 utilizes two differentapproaches to determine whether a pixel is within a “normal” temperaturerange. A first approach compares a temperature value to a statisticaldistribution of pixel temperatures based on historical values for thesame pixel to determine a temperature variance (E.g., historicalvariation map). In most embodiments, a first pixel or first group ofpixels is compared to the same first pixel or group of pixels todetermine if the current temperature is within the historicaltemperature variation (e.g., not oil) or outside the historicaltemperature variation (e.g., oil). In some instances, this protocol canalso include comparing a first pixel (or first group of pixels) to asecond pixel (or second group of pixels) by comparing the pixel values(temperatures) as well as comparing the pixel variations (temperaturevariance) between two regions. Pixels with larger variances compared tothe historical variation map over time can indicate the presence of oil.To the extent the temperature value is within a specified distance(e.g., threshold difference “D”) from a distribution of temperaturevariances, the pixel may be considered within a “normal” range. However,in a scenario that includes surface temperatures changing gradually overtime, such as from throughout the day for when oil contaminates acontrol region, process 420 may not detect a pixel that indicates ahigher temperature rating using this first technique, as the highertemperatures may gradually become a new “normal”, as the highertemperatures may change the nature of the distribution over time (e.g.,over a day, week, month, season, year, etc.). To avoid this possibility,process 420 may compare the temperature value or temperature variationfor a first pixel across multiple images to a threshold value thatdefines a maximum value of normal, regardless of historical values. Bycombining a comparison to historical values and to a threshold value,process 420 provides a robust characterization of a current temperaturevariation value as either “normal” or “abnormal.”

The temperature (i.e., “counts”) difference from the referencebackground has to be large enough that it triggers as a variation. Thisis where the sensitivity factor is considered in the algorithm, wherethe higher the sensitivity, the lower the difference (e.g., difference“D”) between the current pixel temperature value and the referencebackground pixel temperature value is required in order to be consideredas a potential oil pixel (e.g., abnormal). As such, the determination ofan oil pixel based on the difference in temperature for a pixel comparedto the allowable distribution of pixel temperature values is not asimple fixed-threshold relationship, but is based on whether thedifference D falls outside the expected variance observed on that pixelover time. However, some embodiments use the fixed-threshold todetermine normal pixels from abnormal pixels.

In block 422, a temperature value (e.g., temperature variance value) forat least one pixel is received from an imaging sensor or from thetemperature map. In some aspects, the imaging sensor may captureinfrared wavelengths of light and convert the captured light intodigital data which forms an array of temperature values, with a pixeltemperature value for each pixel. The pixel temperature value receivedin block 422 may be one temperature value (temperature variation) of onepixel in the array of temperature values (temperature variation) of aplurality of pixels.

Block 424 determines whether the pixel temperature value (e.g.,temperature value variation) is within a specified distance (e.g.,threshold difference “D”) from a statistical distribution of pixeltemperature values or temperature value variations for each pixel. Thestatistical distribution may be based on historical values of eachpixel. In some aspects, the specified distance from the distribution isa Mahalanobis distance. For example, in some aspects, if the squaredMahalanobis distance is greater than the inverse chi squared cumulativedistribution function at a specified probability (e.g. 0.99), then it iswithin the distribution. Otherwise, it is outside of the distribution insome aspects.

In some aspects, block 424 may make different determinations. Forexample, in some aspects, block 424 may determine whether thetemperature value (e.g., temperature variation for pixel) is within adistance representing 90%, 95%, or 99% of the statistical distribution.If the received value is not within the specified distance from thedistribution, process 420 moves to block 426, which marks the pixel asabnormal in a pixel map (e.g., category map).

If the temperature value is within the specified distance, process 420moves from decision block 424 to decision block 428, which determineswhether the pixel temperature value is above a threshold value (e.g., aset threshold temperature value, which may or may not be the same as thetemperature of the threshold difference D. This determines whether thetemperature variation is greater than a threshold temperature variationfor each pixel. The threshold value referenced in block 428 may be basedon operator configured information, as a set value, or determined overtime based on historical information. The configured information may bespecific to an image (generated by a single imaging sensor or generatedby stitching together data from multiple imaging sensors), or a regionof interest within an image. If the temperature value is above thethreshold value, process 420 moves to block 426, which marks the pixeltemperature value as abnormal (e.g., in category map) as discussedabove.

Otherwise, if the temperature value is within the distance D from thedistribution for the pixel in step 424 or is not greater than thethreshold value in step 428, process 420 moves to block 430, whichrecords the temperature value as normal in the category map.

Due to the historical nature of the data that defines the distributionand thresholds for temperature, the distribution can be updated with thenew data, such as when the new data is marked as normal. Thedistribution is not updated when the pixel temperature value isidentified as being abnormal. Also, the distribution can be anydistribution (e.g., normal Gaussian) and the measurement to thedifference D may be an average, mean, center, edge, or other definedpart of the distribution.

After the distribution is updated in block 432, process 420 moves todecision block 434, which determines if there are more pixels in animage to process. If there are, process 420 returns to block 422 andprocessing continues. If there are no more pixels, processing maycontinue for determining whether there is oil on a surface in theimages.

FIG. 4C includes a flow chart of a process 450 of generating an alertbased on an abnormal region of pixels that are identified as being aregion of oil. In block 452, the temperature category map is receivedindicating normal and abnormal temperature values for each pixel withinthe image. For example, in some aspects, a category map may represent amatrix or two dimensional array of true/false or 1/0 values, with atrue/1 value in a position of the category map indicating a pixellocated at a corresponding position of the image is abnormal, while afalse/0 value in a position of the category map indicates a temperatureor temperature variance located at a corresponding pixel position of theimage is normal. In some aspects, the meaning of these values may bereversed. In some aspects, the category map received in block 452 may begenerated by process 420, discussed above with respect to FIG. 4B.

In block 454, a region of interest with one or more abnormal pixelswithin the image is determined. The region of interest may be determinedin some aspects, by selecting one or more pixels of a previouslyidentified regions of interest. A region of interest can be any regionin the environment that is more susceptible to having oil from an oilleak. The region of interest may also be selected in real time based onan area of abnormal pixels that are adjacent to each other. In someaspects, the region of interest may encompass a subset of all the pixelsin an image. In some aspects, the region of interest may be defined byan operator, for example, by operating a pointing device such as a mouseor touch screen, as well as interacting with the graphical userinterface 200 to identify a portion of the infrared image 205. A regionof abnormal pixels may be identified by connecting a region ofcontiguous or near contiguous abnormal pixels.

Decision block 456 determines whether oil was determined to be presentin the region of interest, where the oil can be a region of abnormalpixels or region of interest in block 454. If no oil in the region ofinterest was identified, then process 450 continues processing. If anoil region was identified in block 456, then process 450 can makedifferent decisions. One decision is that if there is any oil detectedin the images, then the process moves to block 458 and an alert isgenerated. However, the system can be configured to compare any detectedoil (e.g., pixel having oil) to historical values for the pixel(s) or tothreshold values before generating an alert.

In one option, when oil is determined to be present in the pixels of aregion of interest (e.g., when the region of interest is partially orentirely oil), the size of the area of the region of interest (e.g.,size of the area of pixels identified to be oil) is determined andcompared to a threshold area size as shown block 460. When the size ofthe area of the oil is greater than a threshold area size, then theprocess 450 generates the alert 458. When the size of the area of oil isless than a threshold area size, then the alert is not generated andmonitoring for oil or monitoring the size of the region of oilcontinues.

In another option, when oil is determined to be present in the pixels ofa region of interest (e.g., when the region of interest is partially orentirely oil), the size of the area of the region of interest (e.g.,size of the area of pixels identified to be oil) is determined andcompared to a historical area size as shown block 462. The historicalarea size can include an average of historical area sizes for aparticular oil region or averaging across particular oil regions. Forexample, the oil region may be small with a low rate of increasing areasize, the protocol determines whether the current oil region is abovethe historical area sizes or a size that is too different (e.g.,difference, or change in size) from the historical area size. When thesize of the area of the oil is greater than this historical area size ora value to much higher than the historical area, then the process 450generates the alert 458. When the size of the area of oil is within thehistorical area size range or close to the historical area size (e.g.,within a distance/value from the average or range), then the alert isnot generated and monitoring for oil or monitoring the size of theregion of oil continues.

Also, a size of the identified oil region can be compared to apredetermined percent of a region of interest. In some aspects, thepercent of the region of interest may be 1%, 2%, 3%, 4%, 5%, 6%, 7%, 8%,9%, 10%, 15%, 20%, 25%, 30%, 33%, 35%, 50%, 75%, or 100% of the regionof interest. If the area of the oil region is larger than thepredetermined percent, process 450 moves to block 458 where an alert isgenerated.

Some aspects of block 458 may utilize different conditions forgenerating an alert than those described. For example, in some aspects,an absolute size of the oil region (number of adjacent pixels) may beused to determine if an alert should be generated, either to theexclusion of or in conjunction with the size of the oil region relativeto a size of the region of interest.

In some embodiments, the process may calculate an aggregated “normal”temperature (e.g., temperature variation across images) for pixelswithin the abnormal region (e.g., oil region) and an aggregatedtemperature variation within the region of interest. If a distancebetween the aggregated normal temperature variance and aggregatedmeasured temperature variance is above a threshold, an alert may begenerated in some aspects. For example, some aspects may includeselecting a nominal or normal temperature variation from thedistributions for each of the pixels in the abnormal region. Thesenominal values may then be aggregated. Similarly, the measuredtemperatures and temperature variations within the abnormal region maybe separately aggregated. This aggregate of measured temperatures ortemperature variations represents an aggregated variance for theabnormal region. If the measured variance is substantially (representedby the threshold) above a normal variance for the abnormal region, analert may be generated. This technique considers a situation where noneof the pixels within the abnormal region may be above a warning or alertthreshold, and thus, no alert is generated based on these thresholds.Additionally, the abnormal oil region may be a relatively small portionof the region of interest, such that no alert is generated. However,given the number of pixels (within the abnormal oil region) that areabove their nominal or normal points, (i.e. the variance of the abnormaloil region), there may be cause for concern such that an alert isproper.

In some aspects, generating an alert may include displaying a message onan electronic display, such as a system control console. In some otheraspects, generating an alert may include sending an email, text message,or writing data to a log file, or any combination of these.

In some embodiments, a system for detecting an oil leak can include: atleast one infrared imaging sensor; and an imaging analysis computeroperably coupled with the at least one infrared imaging sensor. Theimaging analysis computer can be configured to control any infraredimaging sensor and acquire infrared images therefrom at any rate and inany duration. The imaging analysis computer can be configured to analyzethe infrared images in order to detect an oil leak. The imaging analysiscomputer can be configured to detect oil on a surface where oil shouldnot be (or is not present in a baseline) in order to determine thatthere is an oil leak in the vicinity.

In some embodiments, the system can be configured to obtain at least onebaseline infrared image of a fixed field of view without oil beingpresent. The baseline image can be updated over time prior to oil beingdetected on a surface in the fixed field of view. The baseline image canbe an image from an imaging sensor, or a historical composite of pixeldata from a plurality of baseline images over time. This allows forcomparisons between images with no oil and images that have oil. In someinstances, the at least one baseline image is the historical variationmap, or the one or more images used to prepare the historical variationmap. The at least one baseline infrared image can be a single image whenrepresenting the baseline for each pixel without oil. However, the atleast one baseline image can be a plurality of images, or a compositeprepared from a plurality of images so as to have the distributionthereof (e.g., historical variation map). The at least one baselineinfrared image can provide the threshold difference and thresholdtemperature as well as the allowable pixel variations.

In some embodiments, the system can perform methods to analyze allpixels in the fixed field of view for changes from the at least onebaseline infrared image to at least one subsequent infrared image. Thechanges can be in the pixel data for each pixel, such as changes in thewavelength of the infrared light that indicates changes in temperatureof surfaces emitting the infrared light.

In some embodiments, the system can perform methods to identify variabledifferences in temperatures for each pixel in the field of view betweenthe at least one baseline infrared image and the at least one subsequentinfrared image. The variable difference can be determined by assessingchanges in a specific pixel (e.g., pixel location in the pixel array ofthe imaging device) from a baseline image to a subsequent image.

In some embodiments, the system can perform methods to identify one ormore first pixels in the at least one subsequent infrared image having afirst variable difference in temperature that is greater than anallowable variable difference in temperature for the one or more firstpixels in the at least one subsequent infrared image compared to anallowable variable difference in temperature for the one or more firstpixels in the at least one baseline infrared image. This protocol can beperformed as described in connection to FIG. 4B. Here, the one or morefirst pixels are identified because they have pixel temperature valuesthat are identified as being abnormal because they are outside theallowable variable difference by being greater than the thresholddifference by being above the threshold temperature. The identifiedpixels that are abnormal can be appropriately marked in the categorymap.

In some embodiments, the system can perform methods to determine the oneor more first pixels as being oil based on the first variable differencein temperature of the one or more first pixels being greater than theallowable variable difference in temperature of the one or more firstpixels in the fixed field of view. The pixels that are determined to beoil can be analyzed in accordance with the protocol of FIG. 4C. In someembodiments, the system can perform methods to generate an alert thatidentifies oil being present in the fixed field of view. The generationof the alert and protocol thereof can also be performed in accordancewith the protocol of FIG. 4C.

In some embodiments, the system can perform methods to identify one ormore first pixels in the at least one subsequent infrared image having afirst variable difference in temperature that is greater than a secondvariable difference in temperature for one or more second pixels in theat least one subsequent infrared image compared to the at least onebaseline infrared image. The region of the first pixels can be analyzedto determine the temperature in the baseline image and the subsequentimage, and then determine the change in temperature. Then, the region ofthe second pixels can be analyzed to determine the temperature in thebaseline image and the subsequent image, and then determine the changein temperature. The change in temperature for the first pixels iscompared to the change in temperature for the second pixels. When onegroup of pixels changes more than the other, then it can be determinedthat the surfaces of those pixels changed.

In some embodiments, the system can perform methods to determine the oneor more first pixels as being oil and the one or more second pixels asbeing devoid of oil. This determination can be made based on the firstvariable difference in temperature of the one or more first pixels andthe second variable difference in temperature of the one or more secondpixels in the fixed field of view. When the change in the first pixelsis larger than the change in the second pixels, there is an indicationthat oil is on the surface in the first pixels. Regions where thetemperature variance is similar from the baseline infrared images to thesubsequent images indicate that there hasn't been a change to thesurfaces, and they do not have oil on them.

In some embodiments, the system can perform methods to generate an alertthat identifies the presence of oil in the fixed field of view. In someaspects, the imaging analysis computer is configured to provide thealert. In some aspects, the imaging analysis computer is configured toprovide the alert by actuating an audible and/or visible indicator. Insome aspects, the imaging analysis computer is configured to provide thealert by transmitting the alert to a remote device. In some aspects, thealert is an audible or visible communication.

In some embodiments, the system can perform methods to identify a one ormore first pixels having a variable difference in temperature of from0.5° C. to about 2° C. higher than one or more second pixels in the atleast one subsequent infrared image. A variable difference in this rangefor a group of pixels can indicate the presence of oil. In someinstances, the range may be from 0.25° C. to about 3° C. higher, 0.1° C.to about 2.5° C. higher, or other range indicative of oil being present.

In some embodiments, the imaging analysis computer is configured tomonitor the fixed field of view to detect oil on a solid surface. Thesolid surface can be selected from foliage, wood, plant, soil, rock,concrete, metal, composite, ceramic, plastic, rubber, or combinationthereof. However, other solid or non-liquid (e.g., non-water) surfacesmay be monitored for oil detection. The system can be configured tomonitor certain solid surfaces, such as in an oil processing system orcomponents thereof with or without monitoring the environmentsurrounding the oil processing system or components thereof. Forexample, the system can acquire emissivity, reflectivity, or othersurface characteristics that impact absorption, reflection, emission orother optical light property for surfaces in the fixed field of view.The system can acquire emissivity, reflectivity, or other surfacecharacteristics that impact absorption, reflection, emission or otheroptical light property for surfaces having oil or for oil surfaces.Then, computations can be performed to determine whether there is oil ona surface in the fixed field of view of the baseline and/or subsequentimages.

In some embodiments, the imaging analysis computer is configured tomonitor the fixed field of view to detect oil on water. The watersurface can be analyzed for movement, wave, or stillness, which can beparameterized and included in the calculations. The water surface canalso be analyzed for color, which can be parameterized and included inthe calculations. The system can be configured to monitor certain watersurfaces, such as in or around an oil processing system or componentsthereof. For example, the system can acquire emissivity, reflectivity,or other water surface characteristics for a particular body of waterthat impact absorption, reflection, emission or other optical lightproperty for the water surface in the fixed field of view. The systemcan acquire emissivity, reflectivity, or other surface characteristicsthat impact absorption, reflection, emission or other optical lightproperty for water surfaces having oil or for oil surfaces. Then,computations can be performed to determine whether there is oil on awater surface in the fixed field of view of the baseline and/orsubsequent images.

FIG. 5A illustrates a method 500 of detecting an oil leak. The methodmay be performed with a system described herein having at least oneinfrared imaging sensor and an imaging analysis computer. Step 502includes obtaining at least one baseline infrared image of a fixed fieldof view without oil being present. Step 504 includes analyzing some orall pixels in the fixed field of view for changes from the at least onebaseline infrared image to at least one subsequent infrared image. Step506 can include identifying variable differences in temperatures foreach pixel in the field of view between the at least one baselineinfrared image and the at least one subsequent infrared image. Step 508can include identifying a one or more first pixels in the at least onesubsequent infrared image having a first variable difference intemperature that is greater than allowable based on the distribution oftemperature variances in the at least one subsequent infrared imagecompared to the at least one baseline infrared image (e.g., greater thanthe threshold difference from the distribution or greater than thethreshold temperature). Step 510 can include determining the one or morefirst pixels as being oil, and optionally determining one or more secondpixels as being devoid of oil based on the variable difference intemperature of each pixel in the fixed field of view. Step 512 caninclude generating an alert that identifies the presence of oil in thefixed field of view.

In some embodiments, the method can be performed to include providingthe alert from the imaging analysis computer (step 514). This caninclude any of the following: providing the alert by actuating anaudible and/or visible indicator; providing the alert by transmittingthe alert to a remote device; and/or providing the alert as an audibleor visible communication.

FIG. 5B includes a method 520 for detecting oil on a surface. The method520 can include analyzing a baseline image for a solid surface (step522), and identification of pixels associated with the solid surface.Step 524 can include monitoring the fixed field of view to detect oil ona solid surface. Step 526 can include analyzing a subsequent image foroil on the solid surface. In order to analyze a surface for oil in anyimage, the method may include obtaining an emissivity valuecorresponding to the pixel value for a pixel. In some aspects, theemissivity value may be based on a setting of the imaging sensor. Forexample, in some aspects, the imaging sensor may be configured tocapture objects of a given emissivity. This emissivity value may be usedduring the monitoring and analyzing. In some aspects, an object databasemay include emissivity of known objects, which can be utilized in themethods. In some aspects, an emissivity value of an object beingsearched for in the image may be used. For example, in some aspects thatmay be imaging a steel pipe, an emissivity of steel may be used. Assuch, emissivity for various objects (e.g., from surface of the object)can be obtained, where the objects can be natural plants or objects(e.g., foliage, wood, plant, soil, rock) in the environment or concrete,gravel, metals, composites, plastics, rubber or other industrialsurfaces. The emissivity of different types of oil may also be obtainedfor the data analysis so that oil can be identified as well as theviscosity of the oil being identified. This can allow for determiningthe type of oil. This emissivity value may be configured by an operatorin some aspects.

FIG. 5C includes a method 550 for detecting oil on a water surface. Themethod 550 can include analyzing a baseline image for a water surface(step 552), and identification of pixels associated with the watersurface. Step 554 can include monitoring the fixed field of view todetect oil on a water surface. Step 556 can include analyzing asubsequent image for oil on the water surface. The water surface can beanalyzed for movement, wave, or stillness, which can be parameterizedand included in the calculations. The water surface can also be analyzedfor color, which can be parameterized and included in the calculations.The system can be configured to monitor certain water surfaces, such asin or around an oil processing system or components thereof. Forexample, the system can acquire emissivity, reflectivity, or other watersurface characteristics for a particular body of water that impactabsorption, reflection, emission or other optical light property for thewater surface in the fixed field of view. The system can acquireemissivity, reflectivity, or other surface characteristics that impactabsorption, reflection, emission or other optical light property forwater surfaces having oil or for oil surfaces. Then, computations can beperformed to determine whether there is oil on a water surface in thefixed field of view of the baseline and/or subsequent images.

In some aspects, the method can determine whether or not the water hassurface elevation fluctuations, and compensate for the surface elevationfluctuations during the analysis of the pixels in the fixed field ofview. In some aspect, the method can determine whether or not the waterhas areas of reflected light, and compensate for the areas of reflectedlight during the analysis of the pixels in the fixed field of view.

FIG. 5D shows a protocol 570 for detecting oil on a surface. Theprotocol can include identifying a surface region in the fixed field ofview that is a surface, wherein the surface region has a surfacetemperature (Step 572). Step 574 can include identifying an oil regionin the fixed field of view that is oil by having a variable differencein temperature that is greater than the surface region from the at leastone baseline infrared image to the at least one subsequent infraredimage. The protocol 570 may also determine that the oil region in thefixed field of view in the at least one baseline infrared image and inthe at least one subsequent infrared image have a first difference (Step576). Step 578 can include determining the surface region in the fixedfield of view in the at least one baseline infrared image and in the atleast one subsequent infrared image as having a second difference. Step580 can include determining that the second region in the fixed field ofview is oil when the first difference is greater than the seconddifference.

In some embodiments, the methods can include recording historicalinformation of a plurality of infrared images of the fixed field of viewreceived from the at least one infrared imaging sensor. Such historicalinformation can include the images or image data for a number of imagesover a time period. The historical information can be used forestablishing baselines and controls without oil so that the changes inthe images when oil is present can be detected.

In some embodiments, the methods can include providing the alert on adisplay device. Such a display device can show images selected from: aninfrared image from the at least one infrared sensor; a schematic oflocations of the at least one infrared sensor; or a location of analert.

In some embodiments, the methods can include recalibrating the system,which can be scheduled or as needed or desired. Once the system isrecalibrated, the methods can obtain an updated at least one baselineinfrared image after the recalibration.

In some embodiments, the methods are performed such that the fixed fieldof view includes a hard surface. However, weather can impact whether ornot the hard surfaces have water or any wetness. As such, the method caninclude: determining that it is raining in the fixed field of view; andmonitoring the fixed field of view to detect oil on water, such as whenwater is on a surface. Accordingly, the database may include data foremissivity or other water parameters when on a surface, such as a knownsurface type.

FIG. 5E shows another method 530 for detecting oil on a surface. Themethod 530 can include: associating adjacent first pixels to identify anoil region (step 532); determining a size of the oil region (step 534);and generating an oil region size report that identifies the size of theoil region based on the associated adjacent first pixels (step 536). Themethod 530 may also include associating adjacent first pixels toidentify an oil region; determining an area of the oil region; comparingthe area of the oil region with a threshold area size; and generatingthe alert once the oil region has an area that is at least the size ofthe threshold size, wherein the threshold area size is a defined valueor a percentage of a region of interest.

FIG. 5F shows a protocol 540 for detecting oil on a surface. Theprotocol can include identifying a surface region in the fixed field ofview that is a surface, wherein the surface region has a surfacetemperature (Step 542). Step 544 can include identifying an oil regionin the fixed field of view that is oil by having a variable differencein temperature for each pixel that is greater than the allowablevariable difference in temperature for the surface region from the atleast one baseline infrared image to the at least one subsequentinfrared image. The protocol 540 can also determine the surface regionin the fixed field of view in the at least one baseline infrared imageas being devoid of oil, wherein the surface region has a pixeltemperature value that is within the allowable variable difference intemperature for each pixel (step 546). The protocol 540 can alsodetermine the oil region in the fixed field of view in the at least onesubsequent infrared image as having oil, wherein the oil region havingthe first variable difference in temperature that is greater than theallowable variable difference in temperature for each pixel.

In some embodiments, the methods can include: accessing a memory devicethat includes thermal data for one or more surfaces in the fixed fieldof view; obtaining the thermal data for the one or more surfaces in thefixed field of view; and computing with the thermal data for the one ormore surfaces in the fixed field of view during the analysis of thepixels in the fixed field of view.

In some embodiments, the methods can include: accessing a memory devicethat includes distance data for one or more surfaces in the fixed fieldof view from the at least one infrared imaging sensor: obtaining thedistance data for the one or more surfaces in the fixed field of view;and computing with the distance data for the one or more surfaces in thefixed field of view during the analysis of the pixels in the fixed fieldof view.

In some embodiments, the methods can include determining a relativehumidity; and computing with the relative humidity as data during theanalysis of the pixels in the fixed field of view.

In some embodiments, the imaging analysis computer is configured to:associate adjacent first pixels to identify an oil region; determine asize of the oil region; and generate an oil region size report thatidentifies the size of the oil region based on the associated adjacentfirst pixels. In some aspects, the imaging analysis computer isconfigured to: associate adjacent first pixels to identify an oilregion; determine an area of the oil region; compare the area of the oilregion with a threshold area size; and generate the alert once the oilregion has an area that is at least the size of the threshold size,wherein the threshold area size is a defined value or a percentage of aregion of interest. This protocol can be performed as described herein.

In some embodiments, the imaging analysis computer is configured to:determine whether or not the water has surface elevation fluctuations;and compensate for the surface elevation fluctuations during theanalysis of the pixels in the fixed field of view. In some aspects, theimaging analysis computer is configured to: determine whether or not thewater has areas of reflected light; and compensate for the areas ofreflected light during the analysis of the pixels in the fixed field ofview. This protocol can be performed as described herein.

In some embodiments, the memory device includes thermal data for one ormore surfaces in the fixed field of view, wherein the imaging analysiscomputer is configured to: obtain the thermal data for the one or moresurfaces in the fixed field of view; and compute with the thermal datafor the one or more surfaces in the fixed field of view during theanalysis of the pixels in the fixed field of view. In some aspects, thememory device includes distance data for one or more surfaces in thefixed field of view from the at least one infrared imaging sensor,wherein the imaging analysis computer is configured to: obtain thedistance data for the one or more surfaces in the fixed field of view;and compute with the distance data for the one or more surfaces in thefixed field of view during the analysis of the pixels in the fixed fieldof view. In some aspects, the imaging analysis computer is configuredto: determine a relative humidity; and compute with the relativehumidity during the analysis of the pixels in the fixed field of view.This protocol can be performed as described herein.

In some embodiments, the imaging analysis computer is configured toobtain the at least one baseline infrared image by: acquiring a seriesof infrared images of the fixed field of view; analyzing pixel data ofeach infrared image of the series to determine a pixel temperature foreach pixel for each infrared image; determining a range of pixeltemperatures for each pixel without oil being present in the fixed fieldof view across the series of infrared images of the fixed field of view;and setting the allowable variable difference in temperature to includethe determined range of pixel temperatures for each pixel without oil.In some aspects, the imaging analysis computer is configured to obtainthe at least one baseline infrared image by: performing a statisticalanalysis of the range of pixel temperatures for each pixel without oilbeing present across the series of infrared images of the fixed field ofview to determine an allowable distribution of pixel temperatures foreach pixel; and setting the at least one baseline infrared image so thateach pixel includes the allowable distribution of pixel temperatures.This protocol can be performed as described herein.

In some embodiments, the at least one baseline infrared image is a modelof each pixel with the allowable distribution of pixel temperatures foreach pixel, wherein the model of pixel is obtained by: determining adistribution of the pixel temperatures for each pixel without oil beingpresent across the series of infrared images; identifying a maximumpixel temperature that is greater than the distribution of pixeltemperatures by a first difference; and setting the first differencefrom the distribution to indicate absence of oil for each pixel. Thisprotocol can be performed as described herein. Each pixel can have itsown model based on the historical temperature values.

In some embodiments, the imaging analysis computer is configured to:compare each pixel temperature in the one or more subsequent infraredimages with the model of each pixel with the allowable distribution ofpixel temperatures; determine a difference between each pixeltemperature in the one or more subsequent infrared images and the modelof each pixel; determine whether the difference is greater than athreshold difference, when the difference is greater than the thresholddifference, determine that the pixel is an oil pixel, or when thedifference is less than the threshold difference, determine that thepixel is a surface pixel. In some aspects, the imaging analysis computeris configured to: continuously update the model in real time; andcontinuously compare new infrared images with the model in real time.

In some embodiments, the imaging analysis computer is configured to:determine a standard deviation of the distribution of the pixeltemperatures for each pixel without oil being present across the seriesof infrared images; and set the threshold difference as being a defineddifference from the standard deviation.

In some embodiments, the system can perform a method 700 for detectingviscosity of oil as shown in FIG. 7. The method can include: obtainingat least one baseline infrared image of a fixed field of view withoutoil being present (step 702); analyzing all pixels in the fixed field ofview for changes from the at least one baseline infrared image to atleast one subsequent infrared image (step 704); identifying variabledifferences in temperatures for each pixel in the field of view betweenthe at least one baseline infrared image and the at least one subsequentinfrared image (step 706); identifying one or more first pixels in theat least one subsequent infrared image having a first variabledifference in temperature that is greater than a second variabledifference in temperature for one or more second pixels in the at leastone subsequent infrared image compared to the at least one baselineinfrared image (step 708); determining the one or more first pixels asbeing oil and the one or more second pixels as being devoid of oil basedon the variable difference in temperature of each pixel in the fixedfield of view (step 710); determining an estimated viscosity of the oilin the one or more first pixels based on a comparison of the determinedvariable difference with viscosity data that correlates a variabledifference in temperature with a viscosity (step 712), wherein theviscosity data includes a defined lower viscosity threshold value and adefined upper viscosity threshold value, wherein the estimated viscosityis interpolated between the lower viscosity threshold value and theupper viscosity threshold value (step 712); and generating a report thatidentifies the estimated viscosity for the oil in the fixed field ofview (step 714). The report can then be provided (step 716). In someaspects, the method may further include: determining a type of oilhaving the estimated viscosity; and generating the report to identifythe type of oil

FIG. 8 provides an example infrared image 800 that shows a controlregion 802 without oil on the surface having a first temperature and anoil region 804 with oil on the surface having a second temperature. Atsome baseline time point, oil region 804 was devoid of oil. As such, thevariable temperature difference between the control region 802 and theoil region 804 from a baseline image to a subsequent image can be usedto detect oil on the surface. The difference between the control region802 and the oil region 804 is over 2° C.

In some embodiments, the methods can be operated by software. Thesoftware manages the network connections on a 1 to 1 basis with each IRcamera to monitor camera performance, assigns correct algorithms to eachcamera depending on the solution assigned to the camera, monitors alertsfrom cameras, displays an alert and related IR images for all cameras,assigns CPUs to cameras depending on performance requirements andrecords historical information as determined by the refinery subsystems.The hardware to run the refinery infrared management system can includea multi-CPU racked based system that is scalable to allow for additionalcameras added to each solution. The hardware, memory and disk managementsystem can be scoped and selected based on the final numbers of IRcameras.

The system can contain a series of LCD display screens to show overallmanagement of the infrared system, highlight alert locations as they aretriggered, allow for the display of the IR image from any IR camera, anddisplay operational views of each system such as the tank levelmanagement, thermal component operations, gas and oil leak detection.The display system can utilize the graphical displays from the relativerefinery unit to show locations of IR cameras, IR images and IR alertslocations.

The system can be configured to provide real time alerts for oil leakson any surface as designated by the protocols described herein. In dryapplications without water, the system can include an A615 long wave IRcamera. In wet or marine applications with water, the system can includea cooled long wave IR camera.

The present invention can provide many improvements in oil leakdetection. Some features of the system are: monitors key components andprocesses for oil leaks (e.g., pumps, pipes, flanges and otherconnections); detects oil on any surface including solids or water;detects oil types based on viscosity; provides real time alerts andimages of suspected leaks; if an alert is triggered due to oil beingpresent, the camera can be recalibrated once oil is removed to ensuresetting of the correct baseline image; the system communicates with allcameras to receive radiometric data from images as well as IR variables(temperature, humidity, etc.) from the camera that can be used incalculations and algorithms; the system records and stores 1 image persecond for up to 12 hours or more; an alert will set off an alarm, suchas flash the icon on the system graphical display to designate leaklocation and at user option display the IR image; the system has theability to set tolerances of sensitivity to minimize false alerts; andprovides an average frame rate of 30 frames/sec.

In some embodiments, the methods collect a series of images and analyzesthe images to determine whether one or more abnormal pixels exist in thesame pixel location for some duration. If a specific pixel or region ofpixels only shows as abnormal for a few frames or not for a long enoughduration, it can be determined that the abnormal pixels were anaberration or a non-oil entity. Such a short term duration of anabnormal pixel can be flagged as a potential false alarm.

In some embodiments, the system can be programmed with instructions toperform the methods described herein. The system can also be programmedto track all leak detected locations. Accordingly, once an area orlocation is tagged as an oil leak area, the system can update thedatabase so that this area is monitored as part of a specificallymonitored group. The known leak locations can be routinely monitored andanalyzed for oil leak data, such as source of leak, leak rate, leakvolume, leak viscosity, or other information. The sensitivity of knownleak pixels may be programmed so that system responds to changes in thetemperature appropriately, such as when there are small leaks setting ahigher threshold until the leak is fixed so that an increase in the leakrate or other worsening of the leak can be identified. Another exampleis setting a lower threshold in an area without any leak history.Accordingly, the system can be programmed to accommodate desiredoperability. Additionally, the known leak locations can be tagged formaintenance and maintenance planning. The system can provide real timeupdates on the status of a known leak location, whether or not activelyleaking. When leaking, the system can provide reports for any increasesin leak rate or any other leak change over a period of time. Thesereports can include analytical data for the analyzed leak to provide anyof the leak parameters described herein in real time or over definedtime periods.

In some embodiments, the system can be programmed to automaticallychange flow rate of oil within oil conduits or other oil containing ormoving components. For example, oil is often carried in pipes, throughpumps, and across junctions, any of which may develop a crack or openingthat may leak oil. Once an oil-containing component is identified as asource of the oil leak, the system can automatically regulate the oilamount or oil flow in that component. For example, the system maygenerate an alert of an oil leak, analyze for the location of the oilleak, and then modulate the oil-containing component to regulate theoil, such as by shutting off flow to the leak location. For anotherexample, the system can automatically acute pumps, valves, or otherequipment to modulate, reduce or stop the flow of oil to the oil leaklocation. In another example, the computer can enable an oil valveshutdown for oil leaks that exceed a leak volume, rate or duration,which may be set by the operator to automatically control the valves.

In one embodiment, the type of oil is determined by the location of theoil leak being from a region having a known type of oil. For example, alubricant conduit will leak that lubricant alone. As such, mapping theleak to a component having a known type of oil can result in knowing theviscosity of that type of.

For this and other processes and methods disclosed herein, theoperations performed in the processes and methods may be implemented indiffering order. Furthermore, the outlined operations are only providedas examples, and some operations may be optional, combined into feweroperations, eliminated, supplemented with further operations, orexpanded into additional operations, without detracting from the essenceof the disclosed embodiments.

The present disclosure is not to be limited in terms of the particularembodiments described in this application, which are intended asillustrations of various aspects. Many modifications and variations canbe made without departing from its spirit and scope. Functionallyequivalent methods and apparatuses within the scope of the disclosure,in addition to those enumerated herein, are possible from the foregoingdescriptions. Such modifications and variations are intended to fallwithin the scope of the appended claims. The present disclosure is to belimited only by the terms of the appended claims, along with the fullscope of equivalents to which such claims are entitled. The terminologyused herein is for the purpose of describing particular embodimentsonly, and is not intended to be limiting.

In one embodiment, the present methods can include aspects performed ona computing system. As such, the computing system can include a memorydevice that has the computer-executable instructions for performing themethods. The computer-executable instructions can be part of a computerprogram product that includes one or more algorithms for performing anyof the methods of any of the claims.

In one embodiment, any of the operations, processes, or methods,described herein can be performed or cause to be performed in responseto execution of computer-readable instructions stored on acomputer-readable medium and executable by one or more processors. Thecomputer-readable instructions can be executed by a processor of a widerange of computing systems from desktop computing systems, portablecomputing systems, tablet computing systems, hand-held computingsystems, as well as network elements, and/or any other computing device.The computer readable medium is not transitory. The computer readablemedium is a physical medium having the computer-readable instructionsstored therein so as to be physically readable from the physical mediumby the computer/processor.

There are various vehicles by which processes and/or systems and/orother technologies described herein can be effected (e.g., hardware,software, and/or firmware), and that the preferred vehicle may vary withthe context in which the processes and/or systems and/or othertechnologies are deployed. For example, if an implementer determinesthat speed and accuracy are paramount, the implementer may opt for amainly hardware and/or firmware vehicle; if flexibility is paramount,the implementer may opt for a mainly software implementation; or, yetagain alternatively, the implementer may opt for some combination ofhardware, software, and/or firmware.

The various operations described herein can be implemented, individuallyand/or collectively, by a wide range of hardware, software, firmware, orvirtually any combination thereof. In one embodiment, several portionsof the subject matter described herein may be implemented viaapplication specific integrated circuits (ASICs), field programmablegate arrays (FPGAs), digital signal processors (DSPs), or otherintegrated formats. However, some aspects of the embodiments disclosedherein, in whole or in part, can be equivalently implemented inintegrated circuits, as one or more computer programs running on one ormore computers (e.g., as one or more programs running on one or morecomputer systems), as one or more programs running on one or moreprocessors (e.g., as one or more programs running on one or moremicroprocessors), as firmware, or as virtually any combination thereof,and that designing the circuitry and/or writing the code for thesoftware and/or firmware are possible in light of this disclosure. Inaddition, the mechanisms of the subject matter described herein arecapable of being distributed as a program product in a variety of forms,and that an illustrative embodiment of the subject matter describedherein applies regardless of the particular type of signal bearingmedium used to actually carry out the distribution. Examples of aphysical signal bearing medium include, but are not limited to, thefollowing: a recordable type medium such as a floppy disk, a hard diskdrive (HDD), a compact disc (CD), a digital versatile disc (DVD), adigital tape, a computer memory, or any other physical medium that isnot transitory or a transmission. Examples of physical media havingcomputer-readable instructions omit transitory or transmission typemedia such as a digital and/or an analog communication medium (e.g., afiber optic cable, a waveguide, a wired communication link, a wirelesscommunication link, etc.).

It is common to describe devices and/or processes in the fashion setforth herein, and thereafter use engineering practices to integrate suchdescribed devices and/or processes into data processing systems. Thatis, at least a portion of the devices and/or processes described hereincan be integrated into a data processing system via a reasonable amountof experimentation. A typical data processing system generally includesone or more of a system unit housing, a video display device, a memorysuch as volatile and non-volatile memory, processors such asmicroprocessors and digital signal processors, computational entitiessuch as operating systems, drivers, graphical user interfaces, andapplications programs, one or more interaction devices, such as a touchpad or screen, and/or control systems, including feedback loops andcontrol motors (e.g., feedback for sensing position and/or velocity;control motors for moving and/or adjusting components and/orquantities). A typical data processing system may be implementedutilizing any suitable commercially available components, such as thosegenerally found in data computing/communication and/or networkcomputing/communication systems.

The herein described subject matter sometimes illustrates differentcomponents contained within, or connected with, different othercomponents. Such depicted architectures are merely exemplary, and thatin fact, many other architectures can be implemented which achieve thesame functionality. In a conceptual sense, any arrangement of componentsto achieve the same functionality is effectively “associated” such thatthe desired functionality is achieved. Hence, any two components hereincombined to achieve a particular functionality can be seen as“associated with” each other such that the desired functionality isachieved, irrespective of architectures or intermedial components.Likewise, any two components so associated can also be viewed as being“operably connected”, or “operably coupled”, to each other to achievethe desired functionality, and any two components capable of being soassociated can also be viewed as being “operably couplable”, to eachother to achieve the desired functionality. Specific examples ofoperably couplable include, but are not limited to: physically mateableand/or physically interacting components and/or wirelessly interactableand/or wirelessly interacting components and/or logically interactingand/or logically interactable components.

FIG. 6 shows an example computing device 600 (e.g., a computer) that maybe arranged in some embodiments to perform the methods (or portionsthereof) described herein. In a very basic configuration 602, computingdevice 600 generally includes one or more processors 604 and a systemmemory 606. A memory bus 608 may be used for communicating betweenprocessor 604 and system memory 606.

Depending on the desired configuration, processor 604 may be of any typeincluding, but not limited to: a microprocessor (tP), a microcontroller(tC), a digital signal processor (DSP), or any combination thereof.Processor 604 may include one or more levels of caching, such as a levelone cache 610 and a level two cache 612, a processor core 614, andregisters 616. An example processor core 614 may include an arithmeticlogic unit (ALU), a floating point unit (FPU), a digital signalprocessing core (DSP Core), or any combination thereof. An examplememory controller 618 may also be used with processor 604, or in someimplementations, memory controller 618 may be an internal part ofprocessor 604.

Depending on the desired configuration, system memory 606 may be of anytype including, but not limited to: volatile memory (such as RAM),non-volatile memory (such as ROM, flash memory, etc.), or anycombination thereof. System memory 606 may include an operating system620, one or more applications 622, and program data 624. Application 622may include a determination application 626 that is arranged to performthe operations as described herein, including those described withrespect to methods described herein. The determination application 626can obtain data, such as pressure, flow rate, and/or temperature, andthen determine a change to the system to change the pressure, flow rate,and/or temperature.

Computing device 600 may have additional features or functionality, andadditional interfaces to facilitate communications between basicconfiguration 602 and any required devices and interfaces. For example,a bus/interface controller 630 may be used to facilitate communicationsbetween basic configuration 602 and one or more data storage devices 632via a storage interface bus 634. Data storage devices 632 may beremovable storage devices 636, non-removable storage devices 638, or acombination thereof. Examples of removable storage and non-removablestorage devices include: magnetic disk devices such as flexible diskdrives and hard-disk drives (HDD), optical disk drives such as compactdisk (CD) drives or digital versatile disk (DVD) drives, solid statedrives (SSD), and tape drives to name a few. Example computer storagemedia may include: volatile and non-volatile, removable andnon-removable media implemented in any method or technology for storageof information, such as computer readable instructions, data structures,program modules, or other data.

System memory 606, removable storage devices 636 and non-removablestorage devices 638 are examples of computer storage media. Computerstorage media includes, but is not limited to: RAM, ROM, EEPROM, flashmemory or other memory technology, CD-ROM, digital versatile disks (DVD)or other optical storage, magnetic cassettes, magnetic tape, magneticdisk storage or other magnetic storage devices, or any other mediumwhich may be used to store the desired information and which may beaccessed by computing device 600. Any such computer storage media may bepart of computing device 600.

Computing device 600 may also include an interface bus 640 forfacilitating communication from various interface devices (e.g., outputdevices 642, peripheral interfaces 644, and communication devices 646)to basic configuration 602 via bus/interface controller 630. Exampleoutput devices 642 include a graphics processing unit 648 and an audioprocessing unit 650, which may be configured to communicate to variousexternal devices such as a display or speakers via one or more A/V ports652. Example peripheral interfaces 644 include a serial interfacecontroller 654 or a parallel interface controller 656, which may beconfigured to communicate with external devices such as input devices(e.g., keyboard, mouse, pen, voice input device, touch input device,etc.) or other peripheral devices (e.g., printer, scanner, etc.) via oneor more I/O ports 658. An example communication device 646 includes anetwork controller 660, which may be arranged to facilitatecommunications with one or more other computing devices 662 over anetwork communication link via one or more communication ports 664.

The network communication link may be one example of a communicationmedia. Communication media may generally be embodied by computerreadable instructions, data structures, program modules, or other datain a modulated data signal, such as a carrier wave or other transportmechanism, and may include any information delivery media. A “modulateddata signal” may be a signal that has one or more of its characteristicsset or changed in such a manner as to encode information in the signal.By way of example, and not limitation, communication media may includewired media such as a wired network or direct-wired connection, andwireless media such as acoustic, radio frequency (RF), microwave,infrared (IR), and other wireless media. The term computer readablemedia as used herein may include both storage media and communicationmedia.

Computing device 600 may be implemented as a portion of a small-formfactor portable (or mobile) electronic device such as a cell phone, apersonal data assistant (PDA), a personal media player device, awireless web-watch device, a personal headset device, an applicationspecific device, or a hybrid device that includes any of the abovefunctions. Computing device 600 may also be implemented as a personalcomputer including both laptop computer and non-laptop computerconfigurations. The computing device 600 can also be any type of networkcomputing device. The computing device 600 can also be an automatedsystem as described herein.

The embodiments described herein may include the use of a specialpurpose or general-purpose computer including various computer hardwareor software modules.

Embodiments within the scope of the present invention also includecomputer-readable media for carrying or having computer-executableinstructions or data structures stored thereon. Such computer-readablemedia can be any available media that can be accessed by a generalpurpose or special purpose computer. By way of example, and notlimitation, such computer-readable media can comprise RAM, ROM, EEPROM,CD-ROM or other optical disk storage, magnetic disk storage or othermagnetic storage devices, or any other medium which can be used to carryor store desired program code means in the form of computer-executableinstructions or data structures and which can be accessed by a generalpurpose or special purpose computer. When information is transferred orprovided over a network or another communications connection (eitherhardwired, wireless, or a combination of hardwired or wireless) to acomputer, the computer properly views the connection as acomputer-readable medium. Thus, any such connection is properly termed acomputer-readable medium. Combinations of the above should also beincluded within the scope of computer-readable media.

Computer-executable instructions comprise, for example, instructions anddata which cause a general purpose computer, special purpose computer,or special purpose processing device to perform a certain function orgroup of functions. Although the subject matter has been described inlanguage specific to structural features and/or methodological acts, itis to be understood that the subject matter defined in the appendedclaims is not necessarily limited to the specific features or actsdescribed above. Rather, the specific features and acts described aboveare disclosed as example forms of implementing the claims.

As used herein, the term “determining” encompasses a wide variety ofactions. For example, “determining” may include calculating, computing,processing, deriving, investigating, looking up (e.g., looking up in atable, a database or another data structure), ascertaining and the like.Also, “determining” may include receiving (e.g., receiving information),accessing (e.g., accessing data in a memory) and the like. Also,“determining” may include resolving, selecting, choosing, establishingand the like. Further, a “channel width” as used herein may encompass ormay also be referred to as a bandwidth in certain aspects.

With respect to the use of substantially any plural and/or singularterms herein, those having skill in the art can translate from theplural to the singular and/or from the singular to the plural as isappropriate to the context and/or application. The varioussingular/plural permutations may be expressly set forth herein for sakeof clarity.

It will be understood by those within the art that, in general, termsused herein, and especially in the appended claims (e.g., bodies of theappended claims) are generally intended as “open” terms (e.g., the term“including” should be interpreted as “including but not limited to,” theterm “having” should be interpreted as “having at least,” the term“includes” should be interpreted as “includes but is not limited to,”etc.). It will be further understood by those within the art that if aspecific number of an introduced claim recitation is intended, such anintent will be explicitly recited in the claim, and in the absence ofsuch recitation, no such intent is present. For example, as an aid tounderstanding, the following appended claims may contain usage of theintroductory phrases “at least one” and “one or more” to introduce claimrecitations. However, the use of such phrases should not be construed toimply that the introduction of a claim recitation by the indefinitearticles “a” or “an” limits any particular claim containing suchintroduced claim recitation to embodiments containing only one suchrecitation, even when the same claim includes the introductory phrases“one or more” or “at least one” and indefinite articles such as “a” or“an” (e.g., “a” and/or “an” should be interpreted to mean “at least one”or “one or more”); the same holds true for the use of definite articlesused to introduce claim recitations. In addition, even if a specificnumber of an introduced claim recitation is explicitly recited, thoseskilled in the art will recognize that such recitation should beinterpreted to mean at least the recited number (e.g., the barerecitation of “two recitations,” without other modifiers, means at leasttwo recitations, or two or more recitations). Furthermore, in thoseinstances where a convention analogous to “at least one of A, B, and C,etc.” is used, in general, such a construction is intended in the senseone having skill in the art would understand the convention (e.g., “asystem having at least one of A, B, and C” would include but not belimited to systems that have A alone, B alone, C alone, A and Btogether, A and C together, B and C together, and/or A, B, and Ctogether, etc.). It will be further understood by those within the artthat virtually any disjunctive word and/or phrase presenting two or morealternative terms, whether in the description, claims, or drawings,should be understood to contemplate the possibilities of including oneof the terms, either of the terms, or both terms. For example, thephrase “A or B” will be understood to include the possibilities of “A”or “B” or “A and B.”

In addition, where features or aspects of the disclosure are describedin terms of Markush groups, those skilled in the art will recognize thatthe disclosure is also thereby described in terms of any individualmember or subgroup of members of the Markush group.

As will be understood by one skilled in the art, for any and allpurposes, such as in terms of providing a written description, allranges disclosed herein also encompass any and all possible subrangesand combinations of subranges thereof. Any listed range can be easilyrecognized as sufficiently describing and enabling the same range beingbroken down into at least equal halves, thirds, quarters, fifths,tenths, etc. As a non-limiting example, each range discussed herein canbe readily broken down into a lower third, middle third and upper third,etc. As will also be understood by one skilled in the art all languagesuch as “up to,” “at least,” and the like include the number recited andrefer to ranges which can be subsequently broken down into subranges asdiscussed above. Finally, as will be understood by one skilled in theart, a range includes each individual member. Thus, for example, a grouphaving 1-3 cells refers to groups having 1, 2, or 3 cells. Similarly, agroup having 1-5 cells refers to groups having 1, 2, 3, 4, or 5 cells,and so forth.

From the foregoing, it will be appreciated that various embodiments ofthe present disclosure have been described herein for purposes ofillustration, and that various modifications may be made withoutdeparting from the scope and spirit of the present disclosure.Accordingly, the various embodiments disclosed herein are not intendedto be limiting, with the true scope and spirit being indicated by thefollowing claims.

All references recited herein are incorporated herein by specificreference in their entirety.

1. A system for detecting an oil leak, comprising: at least one infraredimaging sensor; an imaging analysis computer operably coupled with theat least one infrared imaging sensor, wherein the imaging analysiscomputer is configured to: obtain at least one baseline infrared imageof a fixed field of view without oil being present; analyze all pixelsin the fixed field of view for changes from the at least one baselineinfrared image to at least one subsequent infrared image; identifyvariable differences in temperatures for each pixel in the field of viewbetween the at least one baseline infrared image and the at least onesubsequent infrared image; identify one or more first pixels in the atleast one subsequent infrared image having a first variable differencein temperature that is greater than an allowable variable difference intemperature for the one or more first pixels in the at least onesubsequent infrared image compared to an allowable variable differencein temperature for the one or more first pixels in the at least onebaseline infrared image; determine the one or more first pixels as beingoil based on the first variable difference in temperature of the one ormore first pixels being greater than the allowable variable differencein temperature of the one or more first pixels in the fixed field ofview; and generate an alert that identifies oil being present in thefixed field of view.
 2. The system of claim 1, wherein the imaginganalysis computer is configured to provide the alert by: actuating anaudible indicator; actuating a visible indicator; showing the alert on adisplay device; or transmitting the alert to a remote device. 3.-5.(canceled)
 6. The system of claim 1, wherein the imaging analysiscomputer is configured to monitor the fixed field of view to detect oilon a solid surface.
 7. The system of claim 6, wherein the solid surfaceis selected from foliage, wood, plant, soil, rock, concrete, metal,composite, ceramic, plastic, rubber, or combination thereof.
 8. Thesystem of claim 1, wherein the fixed field of view includes at least oneregion of water, wherein the imaging analysis computer is configured tomonitor the fixed field of view to detect oil on the region of water. 9.The system of claim 1, wherein the imaging analysis computer isconfigured to: identify a surface region in the fixed field of view thatis a surface, the surface region having a surface temperature for eachpixel; and identify an oil region in the fixed field of view that is oilby having a variable difference in temperature for each pixel that isgreater than the allowable variable difference in temperature for thesurface region from the at least one baseline infrared image to the atleast one subsequent infrared image.
 10. The system of claim 9, whereinthe imaging analysis computer is configured to: determine the surfaceregion in the fixed field of view in the at least one baseline infraredimage as being devoid of oil, the surface region having the allowablevariable difference in temperature for each pixel; and determine the oilregion in the fixed field of view in the at least one subsequentinfrared image as having oil, the oil region having the first variabledifference in temperature that is greater than the allowable variabledifference in temperature for each pixel. 11.-16. (canceled)
 17. Thesystem of claim 1, wherein the imaging analysis computer is configuredto: associate adjacent first pixels to identify an oil region; determinea size of the oil region; and generate an oil region size report thatidentifies the size of the oil region based on the associated adjacentfirst pixels; or compare the area of the oil region with a thresholdarea size and generate the alert once the oil region has an area that isat least the size of the threshold size, wherein the threshold area sizeis a defined value or a percentage of a region of interest. 18.(canceled)
 19. The system of claim 1, wherein the imaging analysiscomputer is configured to perform at least one of the following:determine whether or not the water has surface elevation fluctuationsand compensate for the surface elevation fluctuations during theanalysis of the pixels in the fixed field of view; determining that itis raining in the fixed field of view and monitoring the fixed field ofview to detect oil on water; determine whether or not the water hasareas of reflected light and compensate for the areas of reflected lightduring the analysis of the pixels in the fixed field of view; obtain thethermal data for the one or more surfaces in the fixed field of view andcompute with the thermal data for the one or more surfaces in the fixedfield of view during the analysis of the pixels in the fixed field ofview; obtain distance data for the one or more surfaces in the fixedfield of view and compute with the distance data for the one or moresurfaces in the fixed field of view during the analysis of the pixels inthe fixed field of view; or determine a relative humidity and computewith the relative humidity during the analysis of the pixels in thefixed field of view. 20.-23. (canceled)
 24. The system of claim 1,wherein the imaging analysis computer is configured to obtain the atleast one baseline infrared image by: acquiring a series of infraredimages of the fixed field of view; analyzing pixel data of each infraredimage of the series to determine a pixel temperature for each pixel foreach infrared image; determining a range of pixel temperatures for eachpixel without oil being present in the fixed field of view across theseries of infrared images of the fixed field of view; and setting theallowable variable difference in temperature to include the determinedrange of pixel temperatures for each pixel without oil.
 25. The systemof claim 24, wherein the imaging analysis computer is configured toobtain the at least one baseline infrared image by: performing astatistical analysis of the range of pixel temperatures for each pixelwithout oil being present across the series of infrared images of thefixed field of view to determine an allowable distribution of pixeltemperatures for each pixel; and setting the at least one baselineinfrared image so that each pixel includes the allowable distribution ofpixel temperatures.
 26. The system of claim 24, wherein the at least onebaseline infrared image is a model of each pixel with the allowabledistribution of pixel temperatures for each pixel, wherein the model ofpixel is obtained by: determining a distribution of the pixeltemperatures for each pixel without oil being present across the seriesof infrared images; identifying a maximum pixel temperature that isgreater than the distribution of pixel temperatures by a firstdifference; and setting the first difference from the distribution toindicate absence of oil for each pixel.
 27. The system of claim 26,wherein the imaging analysis computer is configured to: compare eachpixel temperature in the one or more subsequent infrared images with themodel of each pixel with the allowable distribution of pixeltemperatures; determine a difference between each pixel temperature inthe one or more subsequent infrared images and the model of each pixel;determine whether the difference is greater than a threshold difference,when the difference is greater than the threshold difference, determinethat the pixel is an oil pixel, or when the difference is less than thethreshold difference, determine that the pixel is a surface pixel. 28.(canceled)
 29. The system of claim 27, wherein the imaging analysiscomputer is configured to: determine a standard deviation of thedistribution of the pixel temperatures for each pixel without oil beingpresent across the series of infrared images; and set the thresholddifference as being a defined difference from the standard deviation.30. A method for detecting an oil leak, the method comprising: providingthe system of claim 1; obtaining at least one baseline infrared image ofa fixed field of view without oil being present; analyzing all pixels inthe fixed field of view for changes from the at least one baselineinfrared image to at least one subsequent infrared image; identifyingvariable differences in temperatures for each pixel in the field of viewbetween the at least one baseline infrared image and the at least onesubsequent infrared image; identifying one or more first pixels in theat least one subsequent infrared image having a first variabledifference in temperature that is greater than an allowable variabledifference in temperature for the one or more first pixels in the atleast one subsequent infrared image compared to an allowable variabledifference in temperature for the one or more first pixels in the atleast one baseline infrared image; determining the one or more firstpixels as being oil based on the first variable difference intemperature of the one or more first pixels being greater than theallowable variable difference in temperature of the one or more firstpixels in the fixed field of view; and generating an alert thatidentifies the presence of oil in the fixed field of view.
 31. Themethod of claim 30, further comprising providing the alert from theimaging analysis computer by: actuating an audible indicator; actuatinga visible indicator; showing the alert on a display device; ortransmitting the alert to a remote device. 32.-34. (canceled)
 35. Themethod of claim 30, further comprising monitoring the fixed field ofview to detect oil on a solid surface.
 36. The method of claim 35,further comprising monitoring the solid surface selected from foliage,wood, plant, soil, rock, concrete, metal, composite, ceramic, plastic,rubber, or combination thereof.
 37. The method of claim 30, wherein thefixed field of view includes at least one region of water, the methodfurther comprising monitoring the fixed field of view to detect oil onwater.
 38. The method of claim 30, further comprising: identifying asurface region in the fixed field of view that is a surface, the surfaceregion having a surface temperature; and identifying an oil region inthe fixed field of view that is oil by having a variable difference intemperature for each pixel that is greater than the allowable variabledifference in temperature for the surface region from the at least onebaseline infrared image to the at least one subsequent infrared image.39. The method of claim 38, further comprising: determining the surfaceregion in the fixed field of view in the at least one baseline infraredimage as being devoid of oil, the surface region having the allowablevariable difference in temperature for each pixel; and determining theoil region in the fixed field of view in the at least one subsequentinfrared image as having oil, the oil region having the first variabledifference in temperature that is greater than the allowable variabledifference in temperature for each pixel. 40.-44. (canceled)
 45. Themethod of claim 30, further comprising: associating adjacent firstpixels to identify an oil region; determining a size of the oil region;and generating an oil region size report that identifies the size of theoil region based on the associated adjacent first pixels; or comparingthe area of the oil region with a threshold area size and generate thealert once the oil region has an area that is at least the size of thethreshold size, wherein the threshold area size is a defined value or apercentage of a region of interest.
 46. (canceled)
 47. The method ofclaim 30, further comprising: determining whether or not water hassurface elevation fluctuations and compensating for the surfaceelevation fluctuations during the analysis of the pixels in the fixedfield of view; determining that it is raining in the fixed field of viewand monitoring the fixed field of view to detect oil on water;determining whether or not the water has areas of reflected light andcompensate for the areas of reflected light during the analysis of thepixels in the fixed field of view; obtaining the thermal data for theone or more surfaces in the fixed field of view and compute with thethermal data for the one or more surfaces in the fixed field of viewduring the analysis of the pixels in the fixed field of view; obtainingdistance data for the one or more surfaces in the fixed field of viewand compute with the distance data for the one or more surfaces in thefixed field of view during the analysis of the pixels in the fixed fieldof view; or determining a relative humidity and compute with therelative humidity during the analysis of the pixels in the fixed fieldof view. 48.-51. (canceled)
 52. The method of claim 30, furthercomprising obtaining the at least one baseline infrared image by:acquiring a series of infrared images of the fixed field of view;analyzing pixel data of each infrared image of the series to determine apixel temperature for each pixel for each infrared image; determining arange of pixel temperatures for each pixel without oil being present inthe fixed field of view across the series of infrared images of thefixed field of view; and setting the allowable variable difference intemperature to include the determined range of pixel temperatures foreach pixel without oil.
 53. The method of claim 52, further comprisingobtaining the at least one baseline infrared image by: performing astatistical analysis of the range of pixel temperatures for each pixelwithout oil being present across the series of infrared images of thefixed field of view to determine an allowable distribution of pixeltemperatures for each pixel; and setting the at least one baselineinfrared image so that each pixel includes the allowable distribution ofpixel temperatures.
 54. The method of claim 53, wherein the at least onebaseline infrared image is a model of each pixel with the allowabledistribution of pixel temperatures for each pixel, further comprisingobtaining the model of pixel by: determining a distribution of the pixeltemperatures for each pixel without oil being present across the seriesof infrared images; identifying a maximum pixel temperature that isgreater than the distribution of pixel temperatures by a firstdifference; and setting the first difference from the distribution toindicate absence of oil for each pixel.
 55. The method of claim 54,further comprising: comparing each pixel temperature in the one or moresubsequent infrared images with the model of each pixel with theallowable distribution of pixel temperatures; determining a differencebetween each pixel temperature in the one or more subsequent infraredimages and the model of each pixel; determining whether the differenceis greater than a threshold difference, when the difference is greaterthan the threshold difference, determine that the pixel is an oil pixel,or when the difference is less than the threshold difference, determinethat the pixel is a surface pixel.
 56. (canceled)
 57. The method ofclaim 55, wherein the imaging analysis computer is configured to:determine a standard deviation of the distribution of the pixeltemperatures for each pixel without oil being present across the seriesof infrared images; and set the threshold difference as being a defineddifference from the standard deviation.
 58. A method for detectingviscosity of oil, the method comprising: providing the system of claim1; obtaining at least one baseline infrared image of a fixed field ofview without oil being present; analyzing all pixels in the fixed fieldof view for changes from the at least one baseline infrared image to atleast one subsequent infrared image; identifying variable differences intemperatures for each pixel in the field of view between the at leastone baseline infrared image and the at least one subsequent infraredimage; identifying one or more first pixels in the at least onesubsequent infrared image having a first variable difference intemperature that is greater than a second variable difference intemperature for one or more second pixels in the at least one subsequentinfrared image compared to the at least one baseline infrared image;determining the one or more first pixels as being oil and the one ormore second pixels as being devoid of oil based on the variabledifference in temperature of each pixel in the fixed field of view;determining an estimated viscosity of the oil in the one or more firstpixels based on a comparison of the determined variable difference withviscosity data that correlates a variable difference in temperature witha viscosity, wherein the viscosity data includes a defined lowerviscosity threshold value and a defined upper viscosity threshold value,wherein the estimated viscosity is interpolated between the lowerviscosity threshold value and the upper viscosity threshold value; andgenerating a report that identifies the estimated viscosity for the oilin the fixed field of view.
 59. The method of claim 58, furthercomprising: determining a type of oil having the estimated viscosity;and generating the report to identify the type of oil.